NLP – Word Embeddings – BERT

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based neural network architecture for natural language processing tasks such as text classification, question answering, and language inference. One important feature of BERT is its use of word embeddings, which are mathematical representations of words in a continuous vector space.

In BERT, word embeddings are learned during the pre-training phase and are fine-tuned during the task-specific fine-tuning phase. These embeddings are learned by training the model on a large corpus of text, and they are able to capture semantic and syntactic properties of words.

The BERT model architecture is composed of multiple layers of transformer blocks, with the input being a sequence of tokens (e.g., words or subwords) and the output being a contextualized representation of each token in the sequence. The model also includes a pooled output which is used for many down stream task, which are generated by applying a pooling operation over the entire sequence representation.

How does BERT differ from Word2Vec or GloVe?

Training objective: The main difference between BERT and Word2Vec/GloVe is the training objective. BERT is trained to predict missing words in a sentence (masked language modeling) and predict the next sentence (next sentence prediction), this way the model learns to understand the context of the words. Word2Vec and GloVe, on the other hand, are trained to predict a word given its context or to predict the context given a word, this way the model learn the association between words.
Inputs: BERT takes a pair of sentences as input, and learns to understand the relationship between them, Word2Vec and GloVe only take a single sentence or a window of context words as input.
Directionality: BERT is a bidirectional model, meaning that it takes into account the context of a word before and after it in a sentence. This is achieved by training on both the left and the right context of the word. Word2Vec is unidirectional model which can be trained on either the left or the right context, and GloVe is also unidirectional but it is trained on the global corpus statistics.
Pre-training: BERT is a pre-trained model that can be fine-tuned on specific tasks, Word2Vec and GloVe are also pre-trained models, but the main difference is that their pre-training is unsupervised with no downstream task, this means that the fine-tuning of BERT can provide better performance on some tasks because it is pre-trained with the task objective in mind.

BERT Architecture

The core component of BERT is a stack of transformer encoder layers, which are based on the transformer architecture introduced by Vaswani et al. in 2017. Each transformer encoder layer in BERT consists of multiple self-attention heads. The self-attention mechanism allows the model to weigh the importance of different parts of the input sequence when generating the representation of each token. This allows the model to understand the relationships between words in a sentence and to capture the context in which a word is used.

The transformer architecture also includes a feed-forward neural network, which is applied to the output of each self-attention head to produce the final representation of each token.

Transformer Encoder Layer

In the transformer encoder layer, for every input token in a sequence, the self-attention mechanism computes key, value, and query vectors, which are used to create a weighted representation of the input. The key, value and query vectors are computed by applying different linear transformations (matrix multiplications) on the input embeddings, these linear transformations are learned during the training process.

In BERT, the input representations are computed by combining multiple embedding layers. The input is first tokenized into word pieces, which is a technique that allows the model to handle out-of-vocabulary words by breaking them down into subword units. The tokenized input is then passed through three embedding layers:

Token Embedding: Each word piece is represented as a token embedding
Position Embedding: Each word piece is also represented by a position embedding, which encodes information about the position of the word piece in the input sequence.
Segment Embedding: BERT also uses a segment embedding to represent the input segments, this embedding helps BERT to distinguish between the two sentences when the input is a pair of sentences.

These embeddings are concatenated or added together to obtain a fixed-length vector representation of each word piece. Special tokens [CLS] and [SEP] are used to indicate the beginning and the end of the input segments and the classification prediction respectively, [CLS] token is used as the representation of the entire input sequence, which is used in the classification tasks, while [SEP] token is used to separate the input segments, in the case of the input being a pair of sentences.

Masked Language Modeling

If you try to predict each word of the input sequence using the training data with cross-entropy loss, the learning task becomes trivial for the network. Since the network knows beforehand what it has to predict, it can easily learn weights to reach a 100% classification accuracy.

The masked language modeling (MLM) approach, also known as the “masked word prediction” task, addresses this problem by randomly masking a portion of the input words (e.g., 15%) during training and requiring the network to predict the original value of the masked words based on the context. By masking a portion of the input words, the network is forced to understand the context of the words and to learn meaningful representations of the input.

In the MLM approach, the network is only required to predict the value of the masked words, and the loss is calculated only over the masked words. This means that the model is not learning to predict words it has already seen, but instead, it is learning to predict words it hasn’t seen while seeing all the context around those words.

In addition to MLM, BERT also uses another objective during pre-training called “next sentence prediction” this objective is a binary classification task that is applied on the concatenation of two sentences, the model is trained to predict whether the second sentence is the real next sentence in the corpus or not. This objective helps BERT to understand the relationship between two sentences and how they are related.

NLP – Word Embeddings – FastText

What is the FastText method for word embeddings?

FastText is a library for efficient learning of word representations and sentence classification. It was developed by Facebook AI Research (FAIR).

FastText represents each word in a document as a bag of character n-grams. For example, the word “apple” would be represented as the following character n-grams: “a”, “ap”, “app”, “appl”, “apple”, “p”, “pp”, “ppl”, “pple”, “p”, “pl”, “ple”, “l”, “le”. This representation has two advantages:

It can handle spelling mistakes and out-of-vocabulary words. For example, the model would still be able to understand the word “apple” even if it was misspelled as “appel” or “aple”.
It can handle words in different languages with the same script (e.g., English and French) without the need for a separate model for each language.

FastText uses a shallow neural network to learn the word representations from this character n-gram representation. It is trained using the skip-gram model with negative sampling, similar to word2vec.

FastText can also be used for sentence classification by averaging the word vectors for the words in the sentence and training a linear classifier on top of the averaged vector. It is particularly useful for languages with a large number of rare words, or in cases where using a word’s subwords (also known as substrings or character n-grams) as features can be helpful.

How are word embeddings trained in FastText?

Word embeddings in FastText can be trained using either the skip-gram model or the continuous bag-of-words (CBOW) model.

In the skip-gram model, the goal is to predict the context words given a target word. For example, given the input sequence “I have a dog”, the goal would be to predict “have” and “a” given the target word “I”, and to predict “I” given the target word “have”. The skip-gram model learns to predict the context words by minimizing the negative log likelihood of the context words given the target word.

In the CBOW model, the goal is to predict the target word given the context words. For example, given the input sequence “I have a dog”, the goal would be to predict “I” given the context words “have” and “a”, and to predict “have” given the context words “I” and “a”. The CBOW model learns to predict the target word by minimizing the negative log likelihood of the target word given the context words.

Both the skip-gram and CBOW models are trained using stochastic gradient descent (SGD) and backpropagation to update the model’s parameters. The model is trained by minimizing the negative log likelihood of the words in the training data, given the model’s parameters.

Explain how FastText represents each word in a document as a bag of character n-grams

To represent a word as a bag of character n-grams, FastText breaks the word down into overlapping substrings (also known as character n-grams). For example, the word “apple” could be represented as the following character 3-grams (trigrams): [“app”, “ppl”, “ple”]. The number of characters in each substring is specified by the user and is typically set to between 3 and 6 characters.

For example, consider the following sentence:

“I have a dog”

If we set the number of characters in each substring to 3, FastText would represent each word in the sentence as follows:

“I”: [“I”] “have”: [“hav”, “ave”] “a”: [“a”] “dog”: [“dog”]

The use of character n-grams allows FastText to learn good vector representations for rare words, as it can use the vector representations of the character n-grams that make up the rare word to compute its own vector representation. This is particularly useful for handling out-of-vocabulary words that may not have a pre-trained vector representation available.

How are vector representations for each word computed from n-gram vectors?

In FastText, the vector representation for each word is computed as the sum of the vector representations of the character n-grams (subwords) that make up the word. For example, consider the following sentence:

“I have a dog”

If we set the number of characters in each substring to 3, FastText would represent each word in the sentence as a bag of character 3-grams (trigrams) as follows:

“I”: [“I”] “have”: [“hav”, “ave”] “a”: [“a”] “dog”: [“dog”]

FastText would then learn a vector representation for each character n-gram and use these vector representations to compute the vector representation for each word. For example, the vector representation for the word “have” would be computed as the sum of the vector representations for the character n-grams [“hav”, “ave”].

Since there can be huge number of unique n-grams, how does FastText deal with the memory requirement?

One of the ways that FastText deals with the large number of unique character n-grams is by using hashing to map the character n-grams to a fixed-size hash table rather than storing them in a dictionary. This allows FastText to store the character n-grams in a compact form, which can save memory.

What is hashing? How are character sequences hashed to integer values?

Hashing is the process of converting a given input (called the ‘key’) into a fixed-size integer value (called the ‘hash value’ or ‘hash code’). The key is typically some sort of string or sequence of characters, but it can also be a number or other data type.

There are many different ways to hash a character sequence, but most algorithms work by taking the input key, performing some mathematical operations on it, and then returning the hash value as an integer. The specific mathematical operations used will depend on the specific hashing algorithm being used.

One simple example of a hashing algorithm is the ‘modulo’ method, which works as follows:

Take the input key and convert it into a numerical value, for example by assigning each character in the key a numerical value based on its ASCII code.
Divide this numerical value by the size of the hash table (the data structure in which the hashed keys will be stored).
The remainder of this division is the hash value for the key.

This method is simple and fast, but it is not very robust and can lead to a high number of collisions (when two different keys produce the same hash value). More sophisticated algorithms are typically used in practice to improve the performance and reliability of hash tables.

How is the Skip-gram with negative sampling applied in FastText?

Skip-gram with negative sampling (SGNS) algorithm is used to learn high-quality word embeddings (i.e., dense, low-dimensional representations of words that capture the meaning and context of the words). The Skip-gram with negative sampling algorithm works by training a predictive model to predict the context words (i.e., the words that appear near a target word in a given text) given the target word. During training, the model is given a sequence of word pairs (a target word and a context word) and tries to predict the context words given the target words.

To train the model, the SGNS algorithm uses a technique called negative sampling, which involves sampling a small number of negative examples (random words that are not the true context words) and using them to train the model along with the positive examples (the true context words). This helps the model to learn the relationship between the target and context words more efficiently by focusing on the most informative examples.

The SGNS algorithm steps are as following:

The embedding for a target word (also called the ‘center word’) is calculated by taking the sum of the embeddings for the word itself and the character n-grams that make up the word.
The context words are represented by their word embeddings, without adding the character n-grams.
Negative samples are selected randomly from the vocabulary during training, with the probability of selecting a word being proportional to the square root of its unigram frequency (i.e., the number of times it appears in the text).
The dot product of the embedding for the center word and the embedding for the context word is calculated. We then need to normalize the similarity scores over all of the context words in the vocabulary, so that the probabilities sum to 1 and form a valid probability distribution.
Compute the cross-entropy loss between the predicted and true context words. Use an optimization algorithm such as stochastic gradient descent (SGD) to update the embedding vectors in order to minimize this loss. This involves bringing the actual context words closer to the center word (i.e., the target word) and increasing the distance between the center word and the negative samples.

The cross-entropy loss function can be expressed as:
L = – ∑i(y_i log(p(w_i|c)) + (1 – y_i)log(1 – p(w_i|c)))
where:
L is the cross-entropy loss.
y_i is a binary variable indicating whether context word i is a positive example (y_i = 1) or a negative example (y_i = 0).
p(w_i|c) is the probability of context word i given the target word c and its embedding.
∑i indicates that the sum is taken over all context words i in the vocabulary.

FastText and hierarchical softmax

FastText can use a technique called hierarchical softmax to reduce the computation time during training. Hierarchical softmax works by organizing the vocabulary into a binary tree, with the word at the root of the tree and its descendant words arranged in a hierarchy according to their probability of occurrence.

During training, the model uses the hierarchical structure of the tree to compute the loss and update the model weights more efficiently. This is done by traversing the tree from the root to the appropriate leaf node for each word, rather than computing the loss and updating the weights for every word in the vocabulary separately.

The standard softmax function has a computational complexity of O(Kd), where K is the number of classes (i.e., the size of the vocabulary) and d is the number of dimensions in the hidden layer of the model. This complexity arises from the need to normalize the probabilities over all potential classes in order to obtain a valid probability distribution. The hierarchical softmax reduces the computational complexity to O(d*log(K)). Huffman coding can be used to construct a binary tree structure for the softmax function, where the lowest frequency classes are placed deeper into the tree and the highest frequency classes are placed near the root of the tree.

In the hierarchical softmax function, a probability is calculated for each path through the Huffman coding tree, based on the product of the output vector v_n_i of each inner node n and the output value of the hidden layer of the model, h. The sigmoid function is then applied to this product to obtain a probability between 0 and 1.

The idea of this method is to represent the output classes (i.e., the words in the vocabulary) as the leaves on the tree and to use a random walk through the tree to assign probabilities to the classes based on the path taken from the root of the tree. The probability of a certain class is then calculated as the product of the probabilities along the path from the root to the leaf node corresponding to the class.

This allows the hierarchical softmax function to compute the probability of each class more efficiently, since it only needs to consider the path through the tree rather than the entire vocabulary. This can significantly reduce the computational complexity of the model, particularly for large vocabularies, making it practical to train word embeddings on very large datasets.

Hierarchical softmax and conditional probabilities

To compute the probability of each context word given the center word and its embedding using the hierarchical softmax function, we first organize the vocabulary into a binary tree, with the words at the nodes of the tree and their descendant words arranged in a hierarchy according to their probability of occurrence.

We then compute the probability of each context word by traversing the tree from the root to the appropriate leaf node for the word. For each inner node n in the tree, we compute the probability of traversing the left or right branch of the tree as follows:

p(left|n) = sigmoid(v_n_i · h) p(right|n) = 1 – p(left|n)

where:

v_n_i is the vector representation of inner node n
h is the output value of the hidden layer of the model

The probability of a context word w is then computed as the product of the probabilities of the branches along the path from the root to the leaf node corresponding to w.

NLP – Word Embeddings – GloVe

What are word embeddings?

Word embeddings are a type of representation for text data, which allows words with similar meaning to have a similar representation in a neural network model. Word embeddings are trained such that words that are used in similar contexts will have similar vectors in the embedding space. This is useful because it allows the model to generalize better and makes it easier to learn from smaller amounts of data. Word embeddings can be trained using a variety of techniques, such as word2vec and GloVe, and are commonly used as input to deep learning models for natural language processing tasks.

So are they represented as arrays of numbers?

Yes, word embeddings are typically represented as arrays of numbers. The length of the array will depend on the size of the embedding space, which is a parameter that is chosen when the word embeddings are created. For example, if the size of the embedding space is 50, each word will be represented as a vector of length 50, with each element of the vector representing a dimension in the embedding space.

In a neural network model, these word embedding vectors are typically fed into the input layer of the model, and the rest of the layers in the model are then trained to perform some task, such as language translation or sentiment analysis. The model learns to combine the various dimensions of the word embedding vectors in order to make predictions or decisions based on the input data.

How are word embeddings determined?

There are a few different techniques for determining word embeddings, but the most common method is to use a neural network to learn the embeddings from a large dataset of text. The basic idea is to train a neural network to predict a word given the words that come before and after it in a sentence, using the output of the network as the embedding for the input word. The network is trained on a large dataset of text, and the weights of the network are used to determine the embeddings for each word.

There are a few different variations on this basic approach, such as using a different objective function or incorporating additional information into the input to the network. The specific details of how word embeddings are determined will depend on the specific method being used.

What are the specific methods for generating word embeddings?

Word embeddings are a type of representation for natural language processing tasks in which words are represented as numerical vectors in a high-dimensional space. There are several algorithms for generating word embeddings, including:

Word2Vec: This algorithm uses a neural network to learn the vector representations of words. It can be trained using two different techniques: continuous bag-of-words (CBOW) and skip-gram.
GloVe (Global Vectors): This algorithm learns word embeddings by factorizing a matrix of word co-occurrence statistics.
FastText: This is an extension of Word2Vec that learns word embeddings for subwords (character n-grams) in addition to full words. This allows the model to better handle rare and out-of-vocabulary words.
ELMo (Embeddings from Language Models): This algorithm generates word embeddings by training a deep bi-directional language model on a large dataset. The word embeddings are then derived from the hidden state of the language model.
BERT (Bidirectional Encoder Representations from Transformers): This algorithm is a transformer-based language model that generates contextual word embeddings. It has achieved state-of-the-art results on a wide range of natural language processing tasks.

What is the word2vec CBOW model?

The continuous bag-of-words (CBOW) model is one of the two main techniques used to train the Word2Vec algorithm. It predicts a target word based on the context words, which are the words surrounding the target word in a text.

The CBOW model takes a window of context words as input and predicts the target word in the center of the window. The input to the model is a one-hot vector representation of the context words, and the output is a probability distribution over the words in the vocabulary. The model is trained to maximize the probability of predicting the correct target word given the context words.

During training, the model adjusts the weights of the input-to-output connections in order to minimize the prediction error. Once training is complete, the model can be used to generate word embeddings for the words in the vocabulary. These word embeddings capture the semantic relationships between words and can be used for various natural language processing tasks.

What is the word2vec skip-gram model?

The skip-gram model is the other main technique used to train the Word2Vec algorithm. It is the inverse of the continuous bag-of-words (CBOW) model, which predicts a target word based on the context words. In the skip-gram model, the target word is used to predict the context words.

Like the CBOW model, the skip-gram model takes a window of context words as input and predicts the target word in the center of the window. The input to the model is a one-hot vector representation of the target word, and the output is a probability distribution over the words in the vocabulary. The model is trained to maximize the probability of predicting the correct context words given the target word.

What are the steps for the GloVe algorithm?

GloVe learns word embeddings by factorizing a matrix of word co-occurrence statistics, which can be calculated from a large corpus of text.

The main steps of the GloVe algorithm are as follows:

Calculate the word co-occurrence matrix: Given a large corpus of text, the first step is to calculate the co-occurrence matrix, which is a symmetric matrix X where each element X_ij represents the number of times word i appears in the context of word j. The context of a word can be defined as a window of words around the word, or it can be the entire document.
Initialize the word vectors: The next step is to initialize the word vectors, which are the columns of the matrix W. The word vectors are initialized with random values.
Calculate the pointwise mutual information (PMI) matrix: The PMI matrix is calculated as follows:

PMI_ij = log(X_ij / (X_i * X_j))

where X_i is the sum of all the elements in the ith row of the co-occurrence matrix, and X_j is the sum of all the elements in the jth column of the co-occurrence matrix. The PMI matrix is a measure of the association between words and reflects the strength of the relationship between them.

Factorize the PMI matrix: The PMI matrix is then factorized using singular value decomposition (SVD) or another matrix factorization technique to obtain the word vectors. The word vectors are the columns of the matrix W.
Normalize the word vectors: Finally, the word vectors are normalized to have unit length.

Once the GloVe algorithm has been trained, the word vectors can be used to represent words in a high-dimensional space. The word vectors capture the semantic relationships between words and can be used for various natural language processing tasks.

How is the matrix factorization performed in GloVe? What is the goal?

The goal of matrix factorization in GloVe is to find two matrices, called the word matrix and the context matrix, such that the dot product of these matrices approximates the co-occurrence matrix. The word matrix contains the word vectors for each word in the vocabulary, and the context matrix contains the context vectors for each word in the vocabulary.

To find these matrices, GloVe minimizes the difference between the dot product of the word and context matrices and the co-occurrence matrix using a least-squares optimization method. This results in word vectors that capture the relationships between words in the corpus.

In GloVe, the objective function that is minimized during matrix factorization is the least-squares error between the dot product of the word and context matrices and the co-occurrence matrix. More specifically, the objective function is given by:

How is the objective function minimized?

In each iteration of SGD, a mini-batch of co-occurrence pairs (i, j) is selected from the co-occurrence matrix, and the gradients of the objective function with respect to the parameters are computed for each pair. The parameters are then updated using these gradients and a learning rate, which determines the step size of the updates.

This process is repeated until the objective function has converged to a minimum or a preset number of iterations has been reached. The process of selecting mini-batches and updating the parameters is often referred to as an epoch. SGD is an efficient method for minimizing the objective function in GloVe because it does not require computing the Hessian matrix, which is the matrix of second-order partial derivatives of the objective function.

When should GloVe be used instead of Word2Vec?

GloVe (Global Vectors) and Word2Vec are two widely used methods for learning word vectors from a large corpus of text. Both methods learn vector representations of words that capture the semantics of the words and the relationships between them, and they can be used in various natural language processing tasks, such as language modeling, information retrieval, and machine translation.

GloVe and Word2Vec differ in the way they learn word vectors. GloVe learns word vectors by factorizing a co-occurrence matrix, which is a matrix that contains information about how often words co-occur in a given corpus. Word2Vec, on the other hand, learns word vectors using a shallow neural network with a single hidden layer.

One advantage of GloVe is that it is computationally efficient, as it does not require training a neural network. This makes it well suited for use with large corpora. However, Word2Vec has been shown to perform better on some tasks, such as syntactic analogies and named entity recognition.

How is the co-occurrence matrix reduced to lower dimensions in GloVe?

In GloVe (Global Vectors), the co-occurrence matrix is not directly reduced to lower dimensions. Instead, the co-occurrence matrix is used to learn word vectors, which are then reduced to lower dimensions using dimensionality reduction techniques, such as principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE).

To learn word vectors from the co-occurrence matrix in GloVe, the matrix is factorized into two matrices, called the word matrix and the context matrix, using a least-squares optimization method. The word matrix contains the word vectors for each word in the vocabulary, and the context matrix contains the context vectors for each word in the vocabulary.

After the word vectors have been learned, they can be reduced to lower dimensions using dimensionality reduction techniques. For example, PCA can be used to project the word vectors onto a lower-dimensional space, while t-SNE can be used to embed the word vectors in a two-dimensional space for visualization.

It is worth noting that reducing the dimensionality of the word vectors may result in some loss of information, as some of the relationships between words may be lost in the lower-dimensional space. Therefore, it is important to consider the trade-off between the dimensionality of the word vectors and their representational power.

Interpreting GloVe from the Ratio of Co-occurrence Probabilities

GloVe uses the ratio of co-occurrence probabilities to learn the word vectors and context vectors. Specifically, it minimizes the difference between the dot product of the word and context vectors and the log of the ratio of co-occurrence probabilities. This allows GloVe to learn word vectors that capture the meanings and relationships between words in the language.

Chatting with GPT – Natural Language Processing

Let’s talk about natural language processing. How does input text data get transformed to numerical vectors?

In natural language processing (NLP), input text data is often transformed into numerical vectors in order to make it more suitable for machine learning algorithms to work with. There are several methods that can be used to transform text data into numerical vectors, including:

One-hot encoding: This is a simple method that converts each word in the text into a unique integer index, and then represents each word as a binary vector, with a “1” in the position corresponding to the index of the word and “0”s in all other positions. One-hot encoding is a common method for converting text data into numerical vectors, but it can result in very large and sparse vectors, which may not be efficient to work with.
Word embeddings: Word embeddings are numerical vectors that represent the meaning of a word in a continuous, low-dimensional space. Word embeddings can be trained on large datasets of text data using techniques such as word2vec or GloVe, and they can capture the contextual relationships between words and the similarity between words. Word embeddings are a popular method for representing text data in NLP, as they can capture the meaning of words in a compact and dense form.
Bag of words: The bag of words model represents text data as a fixed-length vector, with each element in the vector representing the number of times a particular word appears in the text. The bag of words model is a simple and commonly used method for representing text data, but it does not capture the order or context of the words in the text.

There are many other methods for converting text data into numerical vectors, and the specific method that is used may depend on the specific NLP task and the goals of the model. It is important to choose an appropriate method for representing text data in order to effectively capture the meaning and context of the text.

What are word2vec word embeddings? How are they trained?

Word2vec is a method for training word embeddings, which are numerical vectors that represent the meaning of a word in a continuous, low-dimensional space. Word embeddings are a popular method for representing text data in natural language processing (NLP), as they can capture the meaning of words in a compact and dense form and can be used as input to machine learning algorithms.

Word2vec is a type of neural network model that is trained to predict the context of a word given a set of surrounding words, or to predict a target word given a set of context words. During training, the model is presented with a large dataset of text data and learns to predict the context of a word based on the words that appear around it. The learned model can then be used to generate word embeddings by taking the weights of the model’s hidden layer as the word vectors.

There are two main variants of the word2vec model: continuous bag of words (CBOW) and skip-gram. In the CBOW model, the goal is to predict the target word given a set of context words, while in the skip-gram model, the goal is to predict the context words given a target word.

Word2vec word embeddings can be trained on large datasets of text data using techniques such as stochastic gradient descent (SGD) or negative sampling.

Provide the exact steps for the Word2Vec algorithm, include equations.

Word2Vec is a technique for learning vector representations of words, also known as “word embeddings”, in a way that captures the context and meaning of the words. The basic idea behind Word2Vec is to use the context of words in a text corpus to predict a target word, based on a sliding window of surrounding words. The prediction task is accomplished using a shallow neural network with a single hidden layer.

Here are the steps for the Word2Vec algorithm:

Preprocess the text data: This involves cleaning and formatting the text data, such as lowercasing, stemming, and removing stop words.
Tokenize the text: This involves dividing the text into individual words or tokens.
Build a vocabulary: This involves creating a list of all the unique words in the text data and assigning each word a unique index.
Create word-context pairs: For each word in the text, create a word-context pair by considering a window of surrounding words. For example, if the window size is 2, the word-context pairs for the word “cat” in the sentence “The cat sat on the mat” would be (“cat”, “The”), (“cat”, “sat”), (“cat”, “on”), and (“cat”, “the”).
Train the model: Use the word-context pairs to train a shallow neural network with a single hidden layer. The input layer consists of the context words, and the output layer consists of the target word. The hidden layer contains a set of “neurons” that represent the word embeddings.

The training process involves minimizing the error between the predicted output (the target word) and the true output (the context words) using stochastic gradient descent. The error is measured using a loss function, such as the negative log likelihood loss.

Here is the equation for the negative log likelihood loss:

Loss = -log P(w | c)

where w is the target word and c is the context.

Extract the word embeddings: Once the model is trained, the word embeddings can be extracted from the hidden layer of the neural network. These word embeddings can then be used for various natural language processing tasks, such as text classification, machine translation, and information retrieval.

You mentioned that word2vec can be trained using negative sampling. How does that work?

Yes, that’s correct! In addition to the traditional Word2Vec algorithm that uses the negative log likelihood loss function, there is also a variant called “negative sampling” that can be used to train the model. Negative sampling is a simplified training method that can be used to learn word embeddings more efficiently, especially for large text corpora.

Here’s how negative sampling works:

Preprocess and tokenize the text data as described in the previous steps.
Build a vocabulary and create word-context pairs as described in the previous steps.
Train the model: For each word-context pair, randomly sample a number of negative examples (i.e., words that are not the target word). The objective is to maximize the probability of the target word given the context words, while minimizing the probability of the negative examples given the context words.

The training process involves minimizing the following loss function:

Loss = -log(sigmoid(u_w * v_c)) – ∑log(sigmoid(-u_n * v_c))

where u_w and v_c are the embeddings for the target word and the context words, respectively, and u_n is the embedding for a negative example. The sigmoid function is used to squash the output to a value between 0 and 1.

Extract the word embeddings: Once the model is trained, the word embeddings can be extracted from the hidden layer of the neural network as described in the previous steps.

So when word2vec is trained using negative sampling, that means that just the loss function changes, otherwise stochastic gradient descent is still used?

Yes, that’s correct. When training word2vec using negative sampling, the loss function changes, but the underlying training algorithm, stochastic gradient descent (SGD), remains the same.

In word2vec, the goal is to learn a set of word vectors that can predict the surrounding words in a text. Negative sampling is a variant of the word2vec model that uses a different loss function to achieve this goal. Specifically, it uses a simplified version of the traditional word2vec loss function that only considers a small, randomly-selected subset of the negative examples (words that are not the target word) at each training step, rather than considering all of the negative examples. This allows the model to be trained more efficiently, as it doesn’t need to compute the loss for all of the negative examples at each training step.

SGD is still used to optimize the model’s parameters (the word vectors) by updating them in the direction that reduces the loss. The key difference with negative sampling is that the loss function being optimized is different from the traditional word2vec loss function.

But basically word2vec is trained using a pretty simple neural network, with just one hidden layer

Yes, that’s correct. Word2vec is typically implemented as a shallow neural network with just one hidden layer. The input to the network is a one-hot encoded word, and the hidden layer is a dense (fully-connected) layer with a fixed number of units. The output of the hidden layer is passed through a softmax activation function to produce a probability distribution over the vocabulary, which represents the model’s prediction of the probability of each word in the vocabulary given the input word.

You mentioned that there are two variants of word2vec – CBOW and skip-gram, what is the difference between the two?

In the Continuous Bag-of-Words (CBOW) model, the goal is to predict the current word given the surrounding context. For example, given the context “the cat sat on,” the model would try to predict the word “mat.” CBOW uses a sliding window approach to build up the context from the surrounding words.

On the other hand, the Skip-Gram model does the opposite: it tries to predict the surrounding context given a current word. So in the same example, given the word “mat,” the model would try to predict the context “the cat sat on.” Skip-gram works well with small amounts of data and is faster to train than CBOW.

What is the algorithms for the CBOW model?

The CBOW model uses a neural network with a single hidden layer to predict the current word based on the surrounding context. The input to the model is a one-hot encoded vector representing the context words, and the output is a probability distribution over the vocabulary, with the goal of predicting the target word with the highest probability.

The model is trained using the negative sampling loss function, which tries to maximize the probability of the target word given the context and minimize the probability of randomly sampled negative examples (words that are not the target word).

Here’s a summary of the CBOW algorithm:

Initialize the model weights and biases.
Iterate through each training example, which consists of a target word and a context of surrounding words.
Convert the context words and target word to one-hot encoded vectors.
Calculate the hidden layer activations by taking the dot product of the input vector (one-hot encoded context words) and the input weight matrix, and adding the bias.
Apply the activation function (e.g. sigmoid or ReLU) to the hidden layer activations to get the hidden layer output.
Calculate the output layer activations by taking the dot product of the hidden layer output and the output weight matrix, and adding the bias.
Apply the softmax function to the output layer activations to get the predicted probability distribution over the vocabulary.
Calculate the loss using the negative sampling loss function, which compares the predicted probability of the target word to the actual label (1 for the target word, 0 for the negative samples).
Backpropagate the error and update the model weights and biases using gradient descent.
Repeat steps 2-9 until the model has converged (i.e. the loss is minimized).

The CBOW algorithm uses the negative sampling loss function to train the model. The negative sampling loss function is defined as:

Loss = -(log(sigmoid(u_o * v_c)) + ∑ log(sigmoid(-u_i * v_c)))
where:

u_o is the output weight vector for the target word.
v_c is the input weight vector for the context words.
u_i is the output weight vector for a negative sample (a word that is not the target word).

The first term in the loss function maximizes the probability of the target word given the context, while the second term minimizes the probability of the negative samples.

The sigmoid function is applied to the dot product of the weight vectors to squash the output to a value between 0 and 1, which can be interpreted as a probability. The log function is applied to the sigmoid output to make the loss function smoother and easier to optimize.

The negative sampling loss function is used because it is computationally more efficient than the full softmax loss function, which involves calculating the probabilities of all words in the vocabulary.

Why is the dot product used? What does it represent?

In the word2vec algorithm, the dot product of the input and output weight vectors is used to measure the similarity between the context words and the target word. The dot product is a measure of the angle between two vectors, and it is defined as the product of the magnitudes of the vectors and the cosine of the angle between them.

In the case of word2vec, the input and output weight vectors are learned embeddings for the context words and the target word, respectively. These embeddings are learned during training, and they represent the underlying relationships between the words in the vocabulary.

The dot product of the input and output weight vectors is then used to predict the probability of the target word given the context. If the dot product is high, it means that the angle between the vectors is small, indicating that the context and target words are similar. This results in a high probability for the target word. On the other hand, if the dot product is low, it means that the angle between the vectors is large, indicating that the context and target words are dissimilar. This results in a low probability for the target word.

Overall, the dot product is used in the word2vec algorithm as a measure of similarity between the context words and the target word, which is used to predict the probability of the target word given the context.

What is the algorithm for the skip-gram model?

The Skip-Gram model is a neural network that uses a single hidden layer to predict the surrounding context given a current word. The input to the model is a one-hot encoded vector representing the current word, and the output is a probability distribution over the vocabulary, with the goal of predicting the context words with the highest probability.

The Skip-Gram model is trained using the negative sampling loss function, which tries to maximize the probability of the context words given the current word and minimize the probability of randomly sampled negative examples (words that are not in the context).

Here’s a summary of the Skip-Gram algorithm:

Initialize the model weights and biases.
Iterate through each training example, which consists of a current word and a context of surrounding words.
Convert the current word and context words to one-hot encoded vectors.
Calculate the hidden layer activations by taking the dot product of the input vector (one-hot encoded current word) and the input weight matrix, and adding the bias.
Apply the activation function (e.g. sigmoid or ReLU) to the hidden layer activations to get the hidden layer output.
Calculate the output layer activations by taking the dot product of the hidden layer output and the output weight matrix, and adding the bias.
Apply the softmax function to the output layer activations to get the predicted probability distribution over the vocabulary.
Calculate the loss using the negative sampling loss function, which compares the predicted probabilities of the context words to the actual labels (1 for the context words, 0 for the negative samples).
Backpropagate the error and update the model weights and biases using gradient descent.
Repeat steps 2-9 until the model has converged (i.e. the loss is minimized).

The negative sampling loss function used in the Skip-Gram model is defined as:

Loss = -(∑ log(sigmoid(u_c * v_o)) + ∑ log(sigmoid(-u_i * v_o)))

where:

u_c is the output weight vector for a context word.
v_o is the input weight vector for the current word.
u_i is the output weight vector for a negative sample (a word that is not in the context).

The first term in the loss function maximizes the probability of the context words given the current word, while the second term minimizes the probability of the negative samples.

In which cases is it better to use the CBOW model vs. Skip-Gram for word2vec?

The Continuous Bag-of-Words (CBOW) model and the Skip-Gram model are two approaches for learning vector representations of words, also known as word embeddings, in natural language processing tasks. These models are trained to predict a target word given the context of surrounding words, using a large corpus of text as input.

The main difference between the CBOW and Skip-Gram models is the way in which they define the context of a word. The CBOW model predicts the target word based on the context provided by the surrounding words, while the Skip-Gram model predicts the surrounding context words based on the target word.

In general, the CBOW model is faster to train and performs better on smaller datasets, while the Skip-Gram model is slower to train but can provide better results on larger datasets. The CBOW model is also better at handling rare words, as it takes into account the context provided by multiple surrounding words, while the Skip-Gram model only considers the context provided by a single target word.

In practice, the choice between the CBOW and Skip-Gram models will depend on the specific application and the available resources. If you have a large dataset and the time and computational resources to train a model, the Skip-Gram model may provide better results. If you have a smaller dataset and need to train a model quickly, the CBOW model may be a better choice.

Chatting with GPT – Neural Networks Cont’d

The information provided about neural networks has been quite useful. I would like to learn more about loss functions. Please explain loss functions in the context of a neural networks – what are some examples of loss functions? How are they used?

Loss functions are a key component of neural networks, as they help to evaluate how well a model is performing. They do this by calculating the difference between the model’s predicted output and the true target output, and then generating a scalar value that represents the model’s overall error.

There are many different loss functions that can be used in neural networks, depending on the specific task at hand. Some common examples include:

Mean Squared Error (MSE) loss: This is a common loss function for regression tasks, where the goal is to predict a continuous numerical value. MSE loss is calculated by taking the average of the squared differences between the predicted output and the true output.
MSE Loss = (1/n) * ∑(ŷ – y)^2
A lower MSE loss indicates that the model is performing better and making more accurate predictions.
Cross-Entropy Loss (CEL): This loss function is commonly used for classification tasks, where the goal is to predict a class label. CEL is calculated by taking the negative log of the predicted probability of the true class.
CEL = – ∑(y * log(ŷ))
A lower CEL indicates that the model is performing better and making more accurate predictions.
Hinge Loss: This loss function is often used for support vector machines (SVMs) and is suitable for binary classification tasks. Hinge loss is calculated by taking the maximum of 0 and the difference between the predicted score for the true class and the predicted score for the false class, plus a margin.
Hinge Loss = max(0, 1 – y * (w * x + b))
where:
y is the true class label (1 if the example belongs to the true class, -1 otherwise)
w and b are the model’s weights and bias, respectively
x is the input example

The value 1 in the equation represents the margin. The margin is a hyperparameter that can be adjusted to control the influence of the loss function. A larger margin results in a more “relaxed” loss function, while a smaller margin results in a more “strict” loss function.
Kullback-Leibler Divergence (KLD) Loss: This loss function is often used in generative models, where the goal is to model the distribution of the data. KLD loss is calculated by taking the difference between the predicted distribution and the true distribution.
KLD Loss = ∑(p(x) * log(p(x)/q(x)))
where q(x) is the predicted distribution and p(x) is the true distribution

In general, loss functions are used to guide the training process in a neural network. During training, the model’s weights and biases are adjusted in an attempt to minimize the loss function. This process is known as backpropagation, and it helps the model to learn patterns in the data and make more accurate predictions.

So once I choose a loss function for my neural network, how will it be used to estimate the weights?

Once you have chosen a loss function for your neural network, it will be used to guide the training process and estimate the weights of the model.

During training, the neural network is presented with a series of input examples, along with the corresponding true output (also known as the target output). The model processes the input and generates a predicted output. The loss function is then used to calculate the difference between the predicted output and the true output, which is known as the error.

The error is then used to update the model’s weights and biases in an attempt to minimize the loss function. This process is known as backpropagation, and it helps the model to learn patterns in the data and make more accurate predictions.

The weights and biases are updated using an optimization algorithm, such as stochastic gradient descent (SGD). During each training iteration, the optimization algorithm adjusts the weights and biases based on the error calculated by the loss function. This process is repeated until the model reaches a satisfactory level of accuracy, or until a specified number of training iterations has been reached.

Provide the steps of how the loss function and errors are used in backpropagation, include equations.

Backpropagation is a process used to train neural networks by adjusting the model’s weights and biases in an attempt to minimize the loss function. The following are the steps involved in backpropagation:

Initialize the model’s weights and biases: Before training can begin, the model’s weights and biases must be initialized. This can be done using a variety of methods, such as setting all weights and biases to 0, or using random values drawn from a normal distribution.
Feed forward: During each training iteration, the model is presented with a series of input examples, along with the corresponding true output (also known as the target output). The model processes the input and generates a predicted output, ŷ, using the following equation:

ŷ = f(w * x + b)

where:

f is the activation function
w and b are the model’s weights and bias, respectively
x is the input example

Calculate the error: The loss function is then used to calculate the difference between the predicted output, ŷ, and the true output, y, which is known as the error. This can be expressed as follows:

Error = Loss(ŷ, y)

where Loss is the chosen loss function.

Backpropagate the error: The error is then used to update the model’s weights and biases in an attempt to minimize the loss function. This is done using an optimization algorithm, such as stochastic gradient descent (SGD).
Adjust the weights and biases: During each training iteration, the optimization algorithm adjusts the weights and biases based on the error calculated by the loss function. This can be expressed as follows:

w’ = w – α * ∂Loss/∂w b’
b’ = b – α * ∂Loss/∂b

where:

w’ and b’ are the updated weights and biases, respectively
α is the learning rate
∂Loss/∂w and ∂Loss/∂b are the partial derivatives of the loss function with respect to the weights and biases, respectively

Repeat until convergence: This process is repeated until the model reaches a satisfactory level of accuracy, or until a specified number of training iterations has been reached.

How do I choose the data on which the model should be trained? How do I split training vs. test data? And what is the difference between training, test, and validation data sets?

When training a neural network, it is important to choose a representative and diverse dataset to ensure that the model is able to learn patterns in the data that generalize well to new, unseen examples.

Once you have chosen a dataset, it is common practice to split it into three sets: training, validation, and test.

Training data is used to fit the model and learn the patterns in the data. The model is presented with a series of input examples, along with the corresponding true output (also known as the target output). The model processes the input and generates a predicted output, and the loss function is used to calculate the difference between the predicted output and the true output. This error is then used to update the model’s weights and biases in an attempt to minimize the loss function. This process is repeated until the model reaches a satisfactory level of accuracy, or until a specified number of training iterations has been reached.
Validation data is used to tune the model’s hyperparameters, such as the learning rate and the regularization coefficients. The model is presented with a series of input examples from the validation set, and the loss function is used to calculate the error between the predicted output and the true output. The hyperparameters are then adjusted in an attempt to minimize the validation loss.
Test data is used to evaluate the model’s generalization performance. The model is presented with a series of input examples from the test set, and the loss function is used to calculate the error between the predicted output and the true output. This error is used to measure the model’s accuracy on new, unseen data.

It is important to keep the training, validation, and test sets separate, as mixing them could result in a model that performs well on the training and validation sets, but poorly on the test set. This could lead to overfitting, which is when a model is overly optimized for the training and validation data, but fails to generalize well to new, unseen examples.

There are a few different strategies for splitting a dataset into training, validation, and test sets. One common approach is to use a 70/15/15 split, where 70% of the data is used for training, 15% is used for validation, and 15% is used for testing. Another approach is to use a 80/10/10 split, where 80% of the data is used for training, 10% is used for validation, and 10% is used for testing. The specific split will depend on the size and diversity of the dataset, as well as the specific requirements of the task at hand.

Can I use a neural network for both regression and classification problem types?

Yes, neural networks can be used for both regression and classification problems.

In a regression problem, the output of the model is a continuous value, such as a price or a probability. The goal is to predict the output value based on the input features. For example, you might use a neural network to predict the price of a house based on its size, location, and other features.

In a classification problem, the output of the model is a categorical value, such as a class label. The goal is to predict the class label of an input sample based on its features. For example, you might use a neural network to predict whether an email is spam or not based on the contents of the email.

Neural networks are very flexible and can be used to solve a wide range of problems. They can be used for both regression and classification, as well as other types of problems such as clustering, generation, and reinforcement learning.

What is meant by regularization in regards to a neural network model?

Regularization is a technique that is used to prevent overfitting in a neural network model. Overfitting occurs when a model is too complex and has too many parameters, which leads to poor generalization to new, unseen data.

There are several ways to regularize a neural network model, including:

L1 regularization: This involves adding a penalty term to the loss function that is proportional to the absolute value of the weights. This has the effect of pushing the weights towards 0, which can help reduce overfitting.
L2 regularization: This involves adding a penalty term to the loss function that is proportional to the square of the weights. This has the effect of pushing the weights towards 0, which can help reduce overfitting.
Dropout: This involves randomly dropping out units (neurons) from the network during training, which can help prevent overfitting by reducing the number of parameters in the model.
Early stopping: This involves stopping the training process early if the model starts to overfit, based on the performance on a validation set.

Regularization is an important technique for improving the generalization performance of a neural network model. It helps the model to generalize better to new, unseen data, rather than just memorizing the training data.

Chatting with GPT – Neural Networks

Chat GPT link

*NOTE: I have manually modified some of the equations produced by ChatGPT, such as adding a t-1 subscript

Hello, please tell me what is ChatGPT?

ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model that was specifically designed for use in chatbots and conversational AI systems. It is trained on a large dataset of human-human conversations and is able to generate human-like responses to user input. ChatGPT can be used to build chatbots that can hold natural language conversations with users, helping businesses and organizations automate customer service, sales, and other types of interactions.

Like other GPT models, ChatGPT is a transformer-based language model that uses attention mechanisms to process and generate text. It is trained using a combination of supervised learning, in which the model is given labeled examples of conversation, and unsupervised learning, in which the model is trained to predict the next word in a sequence based on the context of the words that come before it. This allows ChatGPT to learn the patterns and structure of human conversation and generate appropriate responses to user input.

I would like to learn more about machine learning, let’s start with the concept of neural networks. Please explain the concept of a neural network.

A neural network is a type of machine learning model that is inspired by the structure and function of the human brain. It is composed of a large number of interconnected “neurons,” which are simple processing units that receive input, transform it, and produce output.

In a neural network, the input data is fed through a series of hidden layers, each of which is made up of a large number of neurons. These hidden layers process the input data using weighted connections between the neurons. The weights of these connections are adjusted during the training process, allowing the neural network to “learn” from the input data.

The output of the neural network is produced by the output layer, which is also made up of a number of neurons. The output layer takes the processed data from the hidden layers and produces the final output of the neural network.

Neural networks can be used for a wide variety of machine learning tasks, including classification, regression, and clustering. They are particularly powerful for tasks that require the processing of large amounts of data or the detection of patterns and relationships in data that are not immediately apparent.

Overall, the concept of a neural network is a key element of machine learning and is used in many different applications and industries.

How does a neural network look in terms of mathematical equations? What is the architecture?

The architecture of a neural network refers to the way that the neurons are organized and connected within the network. A typical neural network consists of an input layer, one or more hidden layers, and an output layer. The input layer receives the raw input data, and the output layer produces the final output of the network. The hidden layers are located between the input and output layers and are responsible for processing the input data and producing intermediate output that is passed on to the output layer.

In terms of mathematical equations, the computation performed by a neural network can be represented by a series of matrix multiplications and element-wise non-linear functions (also called activation functions).

For example, consider a neural network with one hidden layer and an input layer with m inputs and an output layer with n outputs. Let’s say that the input data is represented by a matrix X with dimensions m x N (where N is the number of data points) and the weights between the input layer and the hidden layer are represented by a matrix W1 with dimensions m x p (where p is the number of neurons in the hidden layer). The output of the hidden layer is then given by:

h = f(XW1 + b1)

where f is the activation function and b1 is a bias term.

The weights between the hidden layer and the output layer are represented by a matrix W2 with dimensions p x n, and the final output of the neural network is given by:

y = f(hW2 + b2)

where b2 is a bias term.

This is just a simple example, but it illustrates the basic idea of how a neural network can be represented mathematically. In practice, neural networks can have many more layers and a much larger number of neurons, and the equations can become quite complex. However, the basic idea of using matrix multiplications and non-linear activation functions to transform the input data and produce output remains the same.

What exactly do you mean by activation function? What are some examples of such functions?

An activation function is a mathematical function that is applied to the output of a neuron in a neural network. It determines whether the neuron will “fire” and produce an output, or whether it will remain inactive. Activation functions are a key component of neural networks because they allow the network to learn non-linear relationships in the data.

There are many different activation functions that can be used in neural networks, each with its own properties and characteristics. Some common examples include:

Sigmoid: The sigmoid function is a smooth, S-shaped curve that maps any input value to a value between 0 and 1. It is often used in the output layer of a binary classification neural network because it can produce a probability-like output.

Tanh: The tanh (hyperbolic tangent) function is also a smooth, S-shaped curve, but it maps input values to a range of -1 to 1. It is similar to the sigmoid function, but it allows for negative output values as well.

ReLU (Rectified Linear Unit): The ReLU function is a piecewise linear function that maps negative input values to 0 and positive input values to the same value. It is a simple and efficient activation function that has been widely adopted in many neural network architectures.

Leaky ReLU: The leaky ReLU function is a variant of the ReLU function that allows a small negative slope for negative input values. This can help prevent the “dying ReLU” problem, in which some neurons in the network become inactive and stop producing output.

Softmax: The softmax function is a generalization of the sigmoid function that is often used in the output layer of a neural network for multi-class classification. It maps the input values to a probability distribution over the different classes, such that the sum of the probabilities is 1.

These are just a few examples of the many activation functions that are used in neural networks. The choice of activation function can have a significant impact on the performance of the network, and different activation functions may be better suited for different types of tasks and data.

So how would I choose an activation function then? Would each layer have a different activation function?

The choice of activation function can have a significant impact on the performance of a neural network, and it is an important design decision that can affect the network’s ability to learn from the data and generalize to new examples.

There is no one-size-fits-all activation function that is best for all situations, and it is often necessary to experiment with different activation functions to find the one that works best for a particular task. In general, it can be helpful to consider the properties of the activation function and how they align with the characteristics of the data and the goals of the task.

For example, if you are building a neural network for a binary classification task, you may want to use a sigmoid or a tanh activation function in the output layer, since these functions can produce a probability-like output between 0 and 1. On the other hand, if you are building a neural network for a multi-class classification task, you may want to use a softmax activation function in the output layer, since it can produce a probability distribution over the different classes.

As for the hidden layers of the network, ReLU and its variants (such as Leaky ReLU) are often a good choice, since they are simple and efficient and have been shown to work well in a wide range of tasks. However, other activation functions may also be suitable, depending on the characteristics of the data and the specific goals of the task.

In general, it is possible to use different activation functions in different layers of a neural network, and this can sometimes be helpful to improve the network’s performance. However, it is also important to be mindful of the complexity of the network and to avoid adding unnecessary complexity that could harm the network’s ability to learn effectively.

So once I have my data inputs, my data outputs, and I choose my activation functions, how do I choose the number of layers in my neural network?

The number of layers in a neural network, as well as the number of neurons in each layer, are important design choices that can impact the network’s ability to learn from the data and generalize to new examples. In general, the more layers and neurons a network has, the more capacity it has to learn from the data. However, too many layers and neurons can also make the network more prone to overfitting, which is when the network performs well on the training data but poorly on new, unseen examples.

There is no hard and fast rule for choosing the number of layers and neurons in a neural network, and it is often necessary to experiment with different configurations to find the one that works best for a particular task. However, there are a few guidelines that you can follow to help you make informed decisions:

Start with a small number of layers and neurons and gradually increase them until you see improvements in the network’s performance. This can help you avoid the problem of overfitting, since a smaller network is less likely to overfit the data.
Consider the complexity of the task and the amount of data you have available. For more complex tasks or larger datasets, you may need a deeper network (i.e., one with more layers) to learn effectively. On the other hand, for simpler tasks or smaller datasets, a shallower network may be sufficient.
Experiment with different architectures and compare the performance of the different models. This can help you identify the best-performing configuration for your task.

Ultimately, the choice of the number of layers and neurons in a neural network is a trade-off between model capacity and generalization. You want to choose a configuration that has enough capacity to learn from the data, but not so much that it overfits the data. Finding the right balance can require some experimentation and trial and error, but with the right approach, you can build a neural network that performs well on your task.

Once I will choose the number of layers, the number of neurons in each layers, the activation functions, how do I train the model then? How will the weights be calculated?

To train a neural network, you need to adjust the weights of the connections between the neurons in such a way that the output of the network is as close as possible to the desired output for a given set of inputs. The process of adjusting the weights to minimize the difference between the predicted output and the desired output is known as optimization.

There are several optimization algorithms that can be used to train a neural network, each with its own pros and cons. Some common optimization algorithms include:

Stochastic Gradient Descent (SGD): SGD is a simple and widely-used optimization algorithm that involves updating the weights of the network based on the gradient of the loss function with respect to the weights. The loss function is a measure of how well the network is doing at predicting the desired output given the input data, and the gradient is a vector that points in the direction of the steepest increase in the loss function. SGD involves repeatedly updating the weights in the opposite direction of the gradient, in an attempt to minimize the loss function and improve the performance of the network.
Adam (Adaptive Moment Estimation): Adam is a variant of SGD that uses an adaptive learning rate, which means that the step size for updating the weights is adjusted based on the past gradient information. Adam tends to be more efficient and require fewer hyperparameter tuning compared to SGD.
Mini-batch gradient descent: Mini-batch gradient descent is a variant of SGD that involves dividing the training data into small batches and updating the weights based on the average of the gradients of the loss function for each batch. This can be more efficient than SGD, especially for large datasets, since it allows the network to learn from the data more quickly.
RProp (Resilient Backpropagation): RProp is a variant of SGD that uses a heuristic approach to adjust the learning rate for each weight individually, based on the past gradient information. It is generally faster and more efficient than SGD, and it is often used in large-scale neural network training.

Please explain stochastic gradient descent further, include equations

Stochastic Gradient Descent (SGD) is a widely-used optimization algorithm for training neural networks and other machine learning models. It involves updating the weights of the model based on the gradient of the loss function with respect to the weights. The loss function is a measure of how well the model is doing at predicting the desired output given the input data, and the gradient is a vector that points in the direction of the steepest increase in the loss function.

SGD works by iteratively updating the weights of the model in the opposite direction of the gradient, in an attempt to minimize the loss function and improve the performance of the model. The update rule for SGD can be expressed as follows:

w = w – alpha * gradient

where w is the weight of the model, alpha is the learning rate (a hyperparameter that controls the step size for updating the weights), and gradient is the gradient of the loss function with respect to the weight.

The learning rate is an important hyperparameter in SGD, and it determines the step size for updating the weights. A larger learning rate can lead to faster convergence, but it can also make the optimization process more unstable. On the other hand, a smaller learning rate can lead to slower convergence, but it can also make the optimization process more stable.

SGD can be implemented in a number of ways, and it is often used in conjunction with other techniques, such as mini-batch gradient descent or momentum, to improve the efficiency and stability of the optimization process.

You also mentioned ADAM – Adaptive Moment Estimation, how does this algorithm differ from SGD? Please provide equations.

Adam (Adaptive Moment Estimation) is a variant of Stochastic Gradient Descent (SGD) that uses an adaptive learning rate, which means that the step size for updating the weights is adjusted based on the past gradient information. Adam is a popular optimization algorithm that is often used to train neural networks and other machine learning models.

One of the key differences between Adam and SGD is that Adam uses an exponentially weighted average of the past gradients to scale the learning rate, rather than a fixed learning rate as in SGD. This allows Adam to automatically adjust the learning rate based on the characteristics of the data and the optimization process, which can lead to faster convergence and better performance.

The update rule for Adam can be expressed as follows:

m = beta1 * m_t-1 + (1 – beta1) * gradient

v = beta2 * v_t-1 + (1 – beta2) * gradient^2

w = w_t-1 – alpha * m / (sqrt(v) + epsilon)

where w is the weight of the model, alpha is the learning rate (a hyperparameter that controls the step size for updating the weights), m and v are the exponentially weighted averages of the past gradients and squared gradients, respectively, beta1 and beta2 are hyperparameters that control the decay rate of the moving averages, and epsilon is a small value that is used to prevent division by zero.

Adam uses two moving averages, one for the gradients and one for the squared gradients, which allows it to incorporate both the mean and the variance of the past gradients into the learning rate. This can help to stabilize the optimization process and prevent oscillations or divergences.

You also listed RProp (Resilient Backpropagation) as an optimization algorithms, please explain RProp with equations.

Resilient backpropagation (RProp) is an optimization algorithm used in artificial neural networks for training supervised learning models. It is an extension of the backpropagation algorithm that aims to improve the convergence speed of the training process by adapting the learning rate for each weight in the network during training.

Like backpropagation, RProp uses the gradient of the loss function to update the weights of the network. The gradient is calculated using the chain rule and the weights are updated using the following equation:

weight(t+1) = weight(t) – learning_rate * gradient(t)

In backpropagation, the learning rate is a fixed hyperparameter that is set before training. In RProp, the learning rate is not fixed and is instead adapted for each weight based on the sign of the gradient. In RProp, the learning rate for each weight in the network is updated individually, based on the sign of the gradient of the loss function with respect to that weight. This means that the learning rate for each weight is updated individually based on the convergence of that weight, rather than being updated based on the convergence of all weights in the network. This can improve the convergence speed of the training process, as the learning rate can be adjusted according to the convergence of each weight.

The learning rate is updated using the following equation:

learning_rate(i)_t+1 = learning_rate(i)_t – step_size(i)_t-1 * sng (dE_t-1 / dw(i)_t-1 )

I have also often heard the AdaGrad optimizer mentioned, please explain that to me

In AdaGrad, the learning rate is not fixed and is instead adapted for each weight based on the history of the gradients of the loss function with respect to that weight. Specifically, the learning rate is adjusted based on the sum of the squares of the gradients of the loss function with respect to the weight.

The learning rate for each weight is updated using the following equation:

learning_rate(t+1) = learning_rate(t) / sqrt(sum(gradient(t)^2))

where sum(gradient(t)^2) is the sum of the squares of the gradients of the loss function with respect to the weight up to time t.

This means that the learning rate for each weight is inversely proportional to the sum of the squares of the gradients of the loss function with respect to that weight. This has the effect of decreasing the learning rate for weights that have had large gradients in the past, and increasing the learning rate for weights that have had small gradients in the past.

I have also heard of gradient boosting, explain that

Gradient boosting is a machine learning technique for building predictive models by creating an ensemble of weak learners and combining their predictions to create a stronger, more accurate model. The general idea of gradient boosting is to train a sequence of weak learners (e.g. decision trees) in a stage-wise manner, with each subsequent learner trying to correct the errors made by the previous learner. At each stage, the gradient boosting algorithm fits a new weak learner to the residual errors made by the previous learner. The residual errors are the difference between the true values and the predicted values of the target variable.

In gradient boosting, weights are assigned to the incorrectly classified samples in order to give more importance to the samples that are difficult to classify. This helps the weak learners in the ensemble to focus more on the samples that are most likely to improve the overall performance of the model.

The steps of the gradient boosting algorithm can be summarized in the following equations:

Initialize the ensemble with a single weak learner, such as a decision tree, and make predictions using this learner:

prediction(1) = weak_learner_prediction(1)

Calculate the residual errors between the true values and the predicted values of the target variable:

residual_error(t) = true_value(t) – prediction(t)

Train a new weak learner to predict the residual errors from the previous step:

weak_learner_prediction(t+1) = train(weak_learner, residual_error(t))

Update the predictions of the ensemble by adding the predictions of the new weak learner to the previous predictions:

prediction(t+1) = prediction(t) + weak_learner_prediction(t+1)

Repeat steps 2-4 until the desired number of weak learners has been trained.

What is AdaBoost and what are the steps of the algorithm? Include equations.

AdaBoost works by weighting the observations in the training set and adjusting the weight of each observation at each iteration of the algorithm based on the performance of the previous models. The idea behind AdaBoost is to train a sequence of weak learners in an iterative manner, with each learner trying to correct the mistakes of the previous learner. The final model is a weighted average of all the weak models.

Here are the steps of the AdaBoost algorithm:

Initialize the weights of the observations in the training set:
- At the beginning, all the observations in the training set are given the same weight.
Train a weak learner:
- Train a weak learner (e.g., a decision tree) on the training set using the current weights of the observations (My note: sample with replacement).
Calculate the error of the weak learner:
- Calculate the error (my note: total error) on the training set using the following equation:error = sum(weights[i] * I[prediction[i] != y[i]])
where I is the indicator function that returns 1 if the prediction is incorrect and 0 if the prediction is correct, weights is the array of weights for the observations, prediction is the array of predictions made by the weak learner, and y is the array of true labels for the observations.
Calculate the weight of the weak learner:
- Calculate the weight of the weak learner using the following equation :
  alpha = 0.5 * log((1 – total error) / total error)
Adjust the weights of the observations:
- Adjust the weights of the observations using the following equation:
  weights[i+1] = weights[i] * exp(-alpha * I[prediction[i] == y[i]])
Normalize the weights:
- Normalize the weights so that they sum up to 1.
Repeat steps 2-6:
- Repeat steps 2-6 a predetermined number of times or until the error of the model becomes small enough.
Return the final model:
- Return the final model as a weighted average of all the weak models.

My note:

Short note on exp(-alpha * I[prediction[i] == y[i]]) i.e. for misclassification

When the performance is relatively large the last stump did a good job in classifying the records now the new sample weight will be much larger than the old one. When the performance is relatively low the last stump did not do a good job in classifying the records now the new sample weight will only be little larger than the old one.

No misclassification: When the performance is relatively large the last stump did a good job in classifying the records now the new sample weight will be very small than the old one. When the performance is relatively small the last stump did not do a good job in classifying the records now the new sample weight will only be little smaller than the old one.

What is the difference between gradient boosting algorithm and adaptive boosting?

Gradient boosting and adaptive boosting are both ensemble learning algorithms that can be used to improve the performance of a weak learner (a model that performs only slightly better than random guessing) by combining the predictions of multiple weak learners.

The main difference between gradient boosting and adaptive boosting is the way in which the weak learners are trained and combined.

In gradient boosting, the weak learners are trained sequentially, with each learner trying to correct the mistakes of the previous learner. In adaptive boosting (also known as AdaBoost), the weak learners are also trained sequentially, but the training process is different. Each weak learner is trained on a different subset of the training data, and the weights of the training examples are adjusted after each learner is trained to give more weight to the examples that were misclassified. The final prediction is made by combining the predictions of all of the learners using a weighted sum, where the weight of each learner is determined by its accuracy on the training data.

Neural Network Predicting Subreddit Karma and Comments for Mental Health Topics

Introduction:

Problem Description:

This project analyses whether the attributes of the subreddit posts’ content are predictive of social support (karma points and number of comments) posts receive.

Context of the Problem:

Mental health problems tend to go under-reported and under-addressed, which places a high social-economic cost on the society. Research shows that social support is valuable for improving quality of life for people with mental health illnesses.

This project examines what content attributes of anonymous social media posts on reddit platform elicit higher levels of social support in the form of karma points and comments.

Limitation About other Approaches:

We have examined two most relevant papers on the topic, [1] and [2]. Neither Schrading, N. et al. [1], nor De Choudhury, M. & De, S. [2] use subreddit indicator variables (i.e., indicators for schizophrenia, depression, anxiety, etc.) in their analysis. It is likely that posts are treated differently, depending on a mental illness indicated (as per Mann, C. E. & Himelein, M. J. [3], “stigmatization of schizophrenia was significantly higher than stigmatization of depression”). Also, De Choudhury, M. & De, S. [2] used a resource intensive manual labelling approach to arrive at keywords.

Solution:

In this project, the analysis includes subreddit indicators in the neural network model predicting social supports for reddit posts. The figure below shows statistics for subreddit indicators for a sample dataset. It can be seen that the mean for the target variables is very different between subreddits.

Additional inputs include counts of frequent bigrams and emotion labelling of keywords. Emotion labelling was done through an NLP approach, using an already existing emotions lexicon.

Background:¶

Reference	Explanation	Dataset/Input	Weakness
Schrading, N. et al. [1]	They trained and compared multiple classifiers on content of reddit posts to determine the top semantic and linguistic features in detecting abusive relationships.	Subreddit posts with comments that focus on domestic abuse, plus subreddit posts with comments unrelated to domestic abuse as a control set.	Future studies could be implemented on datasets from multiple websites to compare online abuse patterns across forums.
De Choudhury, M. & De, S. [2]	They trained a negative binomial regression model on content of reddit posts (i.e., length, use of 1st pronoun, relationship words, emoticons, positive and negative words, etc.) to predict social support variables (karma points and number of responses).	Posts, comments and associated metadatafrom several mental health subreddits, including alcoholism, anxiety, bipolarreddit, depression,mentalhealth, MMFB (Make Me Feel Better), socialanxiety, SuicideWatch.	– Out of the top 15 discussed predicting variables used in the regression model, the highest coefficient have the intercept and the use of the 1st pronoun. – There is no discussion about correlations between predicting variables (for example, the study uses such variables as negative emotion, positive emotion and number of emoticons, which could be correlated).

Methodology

Schrading, N. et al. [1] reported that out of the post features they analyzed, ngrams were the most predicting ones when detecting abusive relationships in reddit posts. De Choudhury M. & De, S. [2] tried to predict social support variables for mental health related reddit posts using post length, emoticons, unigrams, variables built based on presence of emotionally charged unigrams, etc.

In this project, to predict social support variables (scores and number of comments) for mental health related reddit posts, the model was built using the neural networks approach and with emotionally charged unigrams as indicators of 10 different emotions, emotions count, post length, part of speech frequencies (counts of verbs, pronouns, adverbs and adjectives), count of first pronouns, number of question marks, post length, count of frequent bigrams, and subreddit indicators as predictive variables.

Below is the list of the input used in the models for predicting the score and number of comments:

‘anger’, ‘anticipation’, ‘disgust’, ‘fear’, ‘joy’, ‘negative’, ‘positive’, ‘sadness’, ‘surprise’, ‘trust’,’len_post’, ‘len_post_orig’, ‘first_pronoun_count’, ‘freq_bigram_count’, ‘q_count’, ‘verb_count’, ‘pronoun_count’,’adverb_count’, ‘adjective_count’, Subreddit(display_name=’BipolarReddit’), Subreddit(display_name=’Anxiety’), Subreddit(display_name=’depression’), Subreddit(display_name=’schizophrenia’), Subreddit(display_name=’bipolar’), Subreddit(display_name=’mentalhealth’), Subreddit(display_name=’depression_help’), Subreddit(display_name=’BPD’), Subreddit(display_name=’socialanxiety’), Subreddit(display_name=’mentalillness’)

Emotion lexicon

A public lexicon dataset was used to determine counts of specific emotion words. The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive).

https://nrc.canada.ca/en/research-development/products-services/technical-advisory-services/sentiment-emotion-lexicons

Below are examples of posts with most frequent bigrams highlighted. Frequent bigrams ‘feel like’, ‘feels like’ are consistent with the finding by De Choudhury M. & De, S. [2] of frequent unigrams related to emotional expression.

N-grams

For this project we identified most popular bigrams and trigrams. The counts of most frequent bigrams and trigrams were used while testing various models, and the most useful data turned out to be counts of most frequent 16 bigrams, which were used as one of the inputs to the model.

Below is the list of the most popular bigrams used and a few examples of their usage in raw texts.

Implementation

Data Collection

Obtained data via a public API from 10 mental health subreddits: “depression”, “anxiety”, “bipolarreddit”, “mentalhealth”, “socialanxiety”, “depression_help”, “bipolar”, “BPD”, “schizophrenia”, and “mentalillness”.

First, checking 10 hot posts for each subreddit indicator
Collecting data

top_posts dimensions: (9949, 9)

hot_posts dimensions: (9890, 9)

new_posts dimensions: (9896, 9)

Preparing the Data

reddit data scraping is limited to a maximum of 1000 records per subreddit per each of 3 post categories (“hot”, “top” and “new” posts). To maximize the dataset size, we collected posts of all 3 categories and removed duplicate records that have categories overlapping. As mentioned by De Choudhury M. & De, S. [2], reddit posts reach most of their commentary within the first 3 days from being posted. Thus, we removed posts that were “younger” than 3 days old at the data collection time.

Removing stop words and punctuation
Created ngrams (bigrams, trigrams and fourgrams)
Applying smoothing for trigrams and removing extra words referring to posts, unrelated to this analysis (i.e., moderator’s posts)
Creating emotions dataframe, count POS (part of speech) tags, and topic/subreddit dummies

Reddit score prediction model – results based on first layer weights:
In a multi-layer neural network it is hard to interpret raw internal weights, but it looks like mental health-specific variables (such as indicators for fear or surprise, or subreddit indicators) are more important than generic (such as verb count or the length of the post, which looks to be least useful). In particular most subreddit indicators (“depression_help”, “depression”, “schizophrenia”, etc.), which were not used in other papers, are in top 10 for total weights.

Conclusion and Future Direction¶

In conclusion, neural network results showed that the model inputs do have some predictive power for social response variables ‘number of comments’ and ‘score’, as the sums of weights for input variables were found to be greater than zero. Also during model testing, starting with fewer input variables, adding the rest of the input variables reduced the absolute mean errors.

One of the future improvements for this analysis could be incorporating a variable that indicates whether the post is from a throwaway account or an existing long-term reddit account, as De Choudhury, M. & De, S. [2] mention that reddit’s throwaway accounts allow individuals to express themselves more honestly and to ‘discuss uninhibited feelings’.

Also, while content and length of post titles and how users action on posts (click, read, and reply) might have an impact on post’s score, neither of the research papers cited, nor this analysis used title analysis as a part of the model. As such adding title attributes and post actioning statistics variables to the model could be a potential area for improvement.

References:

[1]: Schrading, N., Alm, C. O., Ptucha, R., & Homan, C. M. An Analysis of Domestic Abuse Discourse on Reddit, The 2015 Conference of Empirical Methods in Natural Language Processing, Lisbon, Portugal, September 2015, pages 2577-2583.

[2]: De Choudhury, M. & De, S. Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity. Eights International AAAI Conference on Weblogs and Social Media, North America, May 2014, pages 71-80. Available at: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8075/8107.

[3] Mann, C. E. & Himelein, M. J. Factors Associated with Stigmatization of Persons with Mental Illness. Psychiatric Services, Vol. 55, No. 2., February 2004, pages 185-197. Available at: https://ps.psychiatryonline.org/doi/pdf/10.1176/appi.ps.55.2.185.