Natural Language Processing with Deep Learning in Python

Explore how feature vectors represent data in natural language processing, using word counts and embeddings to separate categories with geometric boundaries, and examine stop words and tf-idf weighting.

Explore how word analogies arise from word embeddings and vectors, and learn to compute analogies using vector arithmetic and cosine distance.

Explore how tf-idf derived word vectors are reduced with t-SNE and plotted as a scatterplot to test semantic analogies, highlighting the limitations of tf-idf for word relationships.

Explore loading pre-trained glove word vectors from Stanford, compare Euclidean and cosine distances, and compute analogies and nearest neighbors to validate word embeddings in Python.

Explore using pre-trained word2vec vectors with a 3 million vocabulary, including phrases like new_york, and learn to load them with gensim to compute analogies and nearest neighbors.

Apply pre-trained word vectors to text classification by averaging word vectors to create document features in a bag-of-words framework, then train a supervised classifier on the Reuters dataset.

learn to implement text classification using bag-of-words features built from word2vec and glove vectors, with vectorizers, data loading, and a classifier, handling unknown words and empty sentences.

Explore how word embeddings initialize language models using pretrained vectors from word2vec and glove. Experiment with freezing embeddings or keeping them trainable to see effects on rnn performance.

Explore how a suggestion box gathers learner feedback to improve natural language processing with deep learning in Python. Share background, course, difficulty, and topics missing from the syllabus.

Review language modeling with bigrams and Markov models, then compare to neural networks and logistic regression approaches in deep learning, plus practical efficiency tricks for implementation.

Learn to build a simple language model with bigrams and trigrams, using maximum likelihood and add-one smoothing on the Brown corpus.

Explore implementing bigrams in Python by mapping words to indices, applying start and end tokens, smoothing, and scoring real versus fake sentences with normalized log probabilities.

Extend a neural bigram model with logistic regression using one-hot word encoding, train with gradient descent to minimize cross-entropy, and compare weight matrices with the probability matrix.

Explore how logistic regression estimates background probabilities and compares diagram probabilities with count-based estimates in a neural bigram model using a 2000-word vocabulary.

Explore building a neural network bigram model for natural language processing, using two weight layers to compute hidden representations and a softmax output, with training via gradients and cross-entropy.

Explore implementing a neural network bigram model in code, with two-stage training updating w2 and w1, and observe loss convergence and weight plotting.

Boost efficiency in NLP models by using indexing tricks instead of one-hot encoding. Learn how to apply double indexing to compute costs and update weights, enabling faster, memory-efficient training.

Unlock faster natural language processing neural network training by applying an indexing trick to compute hidden values and loss without one-hot inputs, and by reordering training for efficient gradient calculations.

Review language modeling techniques from counting word probabilities to neural networks, link logistic regression to probabilities, and highlight indexing tricks for faster training.

Examine how a bigram neural network models the next word as a language model and builds word embeddings with two V-by-V matrices.

Explore the continuous bag of words cbow model for word prediction, using context word embeddings, mean pooling, and a softmax classifier to predict the middle word.

The skip-gram model predicts context words from an input word, unlike cbow, using either one input with four targets or four samples with one target each, in language modeling.

Explore hierarchical softmax as a scalable alternative to full softmax for large vocabularies, using a binary word tree, sigmoid decisions, and path probabilities, with Huffman coding for frequent words.

Explore negative sampling as a scalable alternative to softmax in natural language processing, using binary cross entropy, sigmoid outputs, and context word sampling within skip gram and cbow frameworks.

Understand how to tune negative sampling as a hyperparameter, typically five to twenty-five, using a smoothed modified unigram distribution, and apply a single negative sample per middle word.

Explore why two word embedding matrices appear in the network. Compare three options: use only the first, concatenate, or average, and note normalization and cosine distance while emphasizing testing.

Explore practical Word2Vec implementation tricks, including selective word dropping, context window effects, learning rate scheduling, and performance tips using parallel processing and native code.

Explore a Word2Vec implementation outline in python, detailing data loading, building and training the model, converting sentences to word indices, applying negative sampling, context windows, and stochastic gradient updates.

Learn to implement word2vec in Python with NumPy, including tokenization, vocabulary building up to 20,000 words with unknown tokens, context windows, negative sampling, and training the model with gradient updates.

Explore why every example in this course uses both Theano and TensorFlow, highlighting first-principles coding in Theano and a production-focused TensorFlow workflow.

Adapt a Word2Vec implementation to TensorFlow using embedding lookup, dot products, and binary cross-entropy with positive and negative samples; compare sampled softmax and hierarchical softmax approaches for efficiency.

Explore the TensorFlow implementation of word2vec in Python, detailing input and output embeddings, negative sampling, and a training loop that collects samples and trains when reaching 128 samples.

Learn how to replace Wikipedia data with the Brown corpus in natural language processing using deep learning in Python, leveraging NLTK utilities, tokenized sentences, and vocabulary limiting.

Explore GloVe, global vectors for word representation, and learn how matrix factorization from recommender systems underpins word vectors for NLP, offering simpler training with fewer hyperparameters.

Explore matrix factorization for recommender systems, including sparse rating matrices, collaborative filtering, and latent features to predict user ratings and drive recommendations.

Train a matrix factorization model by learning two matrices, w and u, whose product approximates the ratings matrix with a least-squares loss via alternating least squares.

Expand matrix factorization with global average, user bias, and movie bias to better predict ratings, and derive update equations for biases and latent features during training.

Apply regularization to a matrix factorization model using the Frobenius norm, deriving update rules for W, U, and bias terms to prevent overfitting.

Learn how GloVe builds word embeddings by constructing a term-term co-occurrence matrix with context distance, applying log scaling and X max weighting, and solving via alternating least squares with biases.

Explore multiple approaches to training GloVe, from alternating least squares and gradient descent to pure Python implementations and deep learning frameworks like PyTorch and TensorFlow, plus GPU acceleration.

Code the GloVe model in Python, build the embedding matrix with a given vocabulary and context size, optimize with gradient descent, and test word analogies.

Continue the glove coding example by implementing alternating least squares, updating w, B, u, and c with regularisation, and comparing vectorized versus non-vectorized solutions for performance.

Implement glove in TensorFlow with gradient descent, leveraging automatic differentiation and built-in optimizers to train a word embedding model from a cooccurrence matrix.

Visualize glove word embeddings with t-SNE to reveal how countries and ethnicities cluster on a scatterplot, highlighting analogies and word proximity in natural language processing with deep learning in Python.

Try new word analogies beyond countries and cities to test your natural language processing model, and tune hyperparameters like learning rate, momentum, embedding dimensionality, and vocabulary size.

Explore unifying word embedding methods by framing Word2Vec and glove as matrix factorization, using pointwise mutual information and negative sampling to derive a PMI-based objective.

Implement the PMI-based word embeddings in Python by building a sparse counts matrix, computing PMI with smoothing, and training embeddings via alternating least squares.

Learn parts of speech tagging with a logistic regression baseline, then improve with recurrent neural networks and hidden Markov models, using sequence data and maximum likelihood.

Explore how logistic regression, hidden Markov models, and recurrent neural networks tackle parts of speech tagging, highlighting context, long term dependencies, and the role of LSTM/GRU units.

Train a baseline parts-of-speech tagger with logistic regression and stochastic gradient descent, compare to a decision tree, and evaluate with accuracy and f1 on Chungking data.

Explore how a recurrent neural network in Theano solves the parts-of-speech tagging problem, using word embeddings, softmax outputs, and F1 score evaluation.

Perform parts of speech tagging using a recurrent neural network in TensorFlow, handling fixed-length sequences with zero padding, leveraging word embeddings and a final classifier.

Explore hidden Markov models for parts of speech tagging, estimating transition and emission probabilities from data and using the Viterbi algorithm to map words to tags.

Learn to implement parts-of-speech tagging with hidden Markov models in Python, smoothing state transition and observation matrices, and evaluate accuracy and F1 on train and test data.

Explore named entity recognition in natural language processing with deep learning in Python, identifying person, company, and location tokens via logistic regression and recurrent networks on tweets.

Frame named entity recognition as the same data format as parts of speech tagging, enabling code reuse with only data loading changes and tagging each word by entity type.

Create a named entity recognition baseline with logistic regression by preparing word-tag data, lowering words, building X and y, and evaluating with a 30 percent test split and F1 scores.

Develop a named entity recognition model with an RNN in Theano for natural language processing using Python and deep learning, and evaluate performance with train and test data.

learn how to perform named entity recognition with an rnn in tensorflow, using the same code as the pos example and reviewing data prep, padding, placeholders, and training loop.

Explore hyperparameter tuning to balance model complexity and accuracy in NLP tasks, emphasizing overfitting risk, the limits of complexity, and tradeoffs between logistic regression baselines and Arnon models.

Explore how bag-of-words falls short and how trees capture sentence structure in language processing. Learn plain recursive neural networks and recursive neural tensor networks, plus memory-efficient tree-to-sequence implementations.

Represent sentences as trees using parts of speech to form noun phrases and verb phrases, then apply sentiment analysis and negation handling, paving the way toward recursive neural networks.

Explore data representations for recursive neural networks by modeling sentences as binary parse trees with words at leaves and phrases at inner nodes, enabling robust sentiment analysis.

Explore the architecture of recursive neural networks for binary and multi-child trees, detailing how shared weights connect to parse tree nodes, compute hidden states, and produce outputs with softmax.

Build a recursive neural network for sentiment analysis using tree structures and word embeddings; implement a forward hidden function and train with stochastic gradient descent, noting challenges from per-tree graphs.

Transform parse trees into sequences to convert recursive neural networks into recurrent neural networks, using three arrays (parents, relations, and words) and post-order traversal to build a scalable tree-to-sequence pipeline.

The lecture extends recursive neural networks to a recursive neural tensor network by introducing quadratic interaction terms between left and right child representations for binary-tree processing.

Learn practical tips for implementing an r n t n in TensorFlow, including using a while loop for symbolic inputs and managing a tensor array of hidden states.

Implement a recursive neural tensor network (RNTN) in TensorFlow, using embeddings, quadratic and linear weights, post-order traversal, and SGD training with regularization for root-node predictions.

Explore a practical recursive neural network implemented in TensorFlow, using post-order traversal, tree-based graphs, and a custom training loop with Savir to save and load weights.

Discover Theano basics, including symbolic variables and tensors of various shapes, and build functions. Learn automatic gradients with shared variables and a simple training loop to minimize a cost.

Implement a Theano neural network in code using softmax, define a cost function with regularisation, and train with placeholders and shared variables, then evaluate predictions on the test set.

Explore TensorFlow basics, including placeholders, variables, sessions, and simple matrix multiplication, then minimize a cost function with gradient descent using a learning rate of 0.3.

Build a TensorFlow neural network with a second hidden layer of 100 units, configure placeholders and variables, and train using momentum-based optimizers while monitoring cost and potential overfitting.

Clarify the appendix and FAQ in natural language processing with deep learning in Python, highlighting supplementary information and the optional nature of this section.