Deep Learning: Advanced Natural Language Processing and RNNs

Review foundational deep learning and natural language processing concepts through text-based NLP problems, including word embeddings, CNNs for text, and RNNs using Keras.

Learn to open plain text files on Windows using utf-8 encoding with the open function, and avoid common pitfalls from compressed files and GitHub downloads by cloning repositories.

Discover how word embeddings convert words into numerical feature vectors, forming a vocabulary size by embedding dimension matrix that maps vocabulary indices to dense representations, enabling efficient neural network input.

Explore how pre-trained word embeddings like Word2Vec and GloVe enable transfer learning in NLP by initializing neural networks with external embeddings, handling out-of-vocabulary words, and testing fine-tuning in Keras.

Explore how 1-D convolution on word embeddings enables nlp in a simple, trainable architecture built in Keras, covering cross-correlation, pooling, and a sequence-modeling convolutional neural network.

Download the toxic comments dataset from Kaggle and train a single neural network with six binary outputs, using sigmoid activations for toxic, severe_toxic, obscene, threat, insult, and identity_hate.

Explore a cnn for text classification in Python using Keras, covering tokenization, padding, and embedding with pre-trained GloVe vectors. Preprocess data with word2vec mappings and vocabulary handling.

Train a cnn for text classification with pre-trained word embeddings, three convolution layers, three max pools, a global max pool, and six binary sigmoid outputs.

Explore how a simple recurrent unit adds memory to neural networks, using shared weights (Elman unit) to process sequences and compare with fixed-size feedforward models in Keras and TensorFlow.

Examine GRUs and LSTMs, their gates and two states, and how they address vanishing gradients to model long-term dependencies in language. Learn practical Keras usage for return_state and return_sequences.

Discover how RNN task types depend on input and output shapes, from one-to-one and many-to-one to many-to-many and one-to-many, using Tx by D, Ty by K.

Explore how return_sequences and return_states shape LSTM and GRU outputs, including hidden and cell states, and verify results with a simple rnn test.

Build an LSTM-based rnn for toxic comment classification using embeddings, max sequence length, and pooling, with sigmoid output and higher auc and accuracy despite slower training.

Review the core concepts of word embeddings, CNNs, and RNNs, including GRU and LSTM, and apply them to toxic comment classification using pre-trained embeddings like Word2Vec, GloVe, or FastText.

The lecture invites learners to share feedback via a simple suggestion box, guiding improvements for the deep learning NLP and RNNs course through targeted questions at lazyprogrammer.me/suggestions.

Explore bidirectional RNNs by concatenating forward and backward hidden states to form the output, improving NLP sequence labeling, with guidance on Keras wrappers and debugging return options.

Explore the Keras bidirectional LSTM API by running experiments in bilstm test.py with return_sequences true and false, inspecting outputs, and comparing forward and backward hidden and cell states.

Explore the code for a bidirectional LSTM on the toxic comments dataset, compare with a unidirectional LSTM, and note CPU vs GPU performance and library differences (TensorFlow, PyTorch, Keras).

Explore how bidirectional RNNs classify images by treating an image as a sequence of pixels, using vertical and horizontal scans, global max pooling, and concatenation for softmax classification.

Implements a dual bidirectional LSTM for image classification on MNIST, using bilstm_mnist.py, with concatenation, permute_dimensions, a lambda layer, and sparse categorical cross-entropy.

Explore bidirectional RNNs in Keras, showing outputs as the last forward hidden state concatenated with the first backward hidden state, and demonstrate an image classification application.

Explore sequence-to-sequence models, a dual-RNN encoder-decoder that compresses input into a thought vector and unfolds it into a variable-length output. Apply to translation and other seq-to-seq tasks.

Explore Seq2Seq applications like machine translation, question answering, and chatbots, learn how input-output word sequences form thought vector for decoding; understand why conversation requires memory beyond simple Seq2Seq.

Explore decoding in detail within seq2seq architectures, and implement teacher forcing to train decoders with true previous words, addressing constant-size inputs in Keras and dual training and sampling models.

Revisit poetry generation in Keras to learn language modeling with RNNs, focusing on next-word prediction, SOS and EOS tokens, and the seq2seq decoder–encoder relationship via teacher forcing.

train a language model for poetry generation with poetry.py, using start and end tokens, tokenizer, padding, and an lstm-based seq2seq-like model, and compare greedy translation to sampling from posterior distributions.

Explore poetry generation with an end-to-end sampling approach in advanced NLP deep learning, reusing trained LSTM layers to predict next words, manage hidden states, and print four-line verses.

Learn neural machine translation with a seq2seq model using an encoder LSTM and a decoder, handling two vocabularies, tokenizers, and SOS/EOS tokens, trained via teacher forcing.

Implement a sampling seq2seq for neural machine translation by wiring the encoder and decoder LSTMs, defining initial states, and performing greedy one-word decoding with SOS and EOS tokens.

Explore Seq2Seq, the encoder-decoder approach for translation and question answering, and how decoding with teacher forcing, maxpooling, and bidirectional RNNs overcome fixed-vector limits.

Introduce attention in recurrent neural networks, comparing final-output predictions with mid-sequence hidden-state weighting using softmax. Explain hardmax versus softmax and why attention matters.

Explore how attention works in seq2seq models by computing a context vector from encoder hidden states via attention weights, guiding the decoder LSTM to translate with bidirectional encoders.

Explains reconciling teacher forcing with attention by concatenating the previous word and the context vector at the decoder input, for training and inference.

Implement attention in Keras from scratch, outlining encoder-decoder architecture, context vectors, alphas, and softmax over time, while emphasizing shapes and creating robust, version-safe code.

Explore attention in neural machine translation with code, implementing a bi-directional encoder and a decoder using teacher forcing, attention context vectors, and a two-layer network with softmax over time.

Finish off attention script with a one-step encoder–decoder model that uses an attention context vector and embedding to predict word probabilities. Initialize s and c to zeros, and avoid loops.

Explore how attention weights reveal which input parts influence each output step by visualizing alpha as a t by t' matrix, and interpret near linear patterns in translations.

Build a chatbot without writing code by converting Twitter conversation data into the right format and training a model, highlighting data-driven input and output patterns and ESL dialogue.

Learn how attention augments seq2seq by weighting encoder states with a neural network, enabling end-to-end differentiable training for long sequences.

Introduce memory networks for story-based question answering, using story, question, and answer inputs; link to bAbI data, attention mechanisms, and word embeddings.

Explore memory networks for reading comprehension, building sentence embeddings by summing word vectors and scoring attention-based relevance with dot products and softmax, including one- and two-hop models for supporting facts.

Explore memory networks in code by loading triplets of stories, questions, and answers, training single and two supporting facts models, and visualizing attention weights across story lines.

Build and interpret a memory network for a single supporting fact by embedding sentences, computing story weights with dot products and softmax, and visualizing results with a debug model.

Extend memory networks to two supporting facts stories using embed and sum for story and question representations, with two hops and a dense elu-activated layer.

Explore memory networks that retain past information to answer questions about stories from the bAbI dataset. Use attention with softmax and vector representations, with optional rnn-based hops, to derive answers.

Explore how Keras, a high-level library built on Theano, TensorFlow, and CNTK, enables quick convolutional and dense neural network construction with activation layers, while noting its limits for deep understanding.

Learn to build a Keras sequential neural network with two hidden layers (500 and 300), using dense and activation, and train with the fit function and multi-class cross entropy loss.

Learn the Keras functional API for building neural networks with model, input, and dense layers, offering a compact alternative to sequential. Build a model by connecting input to output.

Learn to convert Keras code to TensorFlow 2.0 using the built-in Keras API, with minimal import changes and a cnn text classification example.

Learn the appendix, or faq (frequently asked questions), as optional supplementary material not part of main content, offering answers to common questions and clarifying course topics.

Understand installation lectures as guidelines and focus on principles over syntax when installing Python and deep learning libraries like CNTK, Theano, and OpenAI Gym.

Discover how to install data science libraries on Windows with Anaconda, including NumPy, SciPy, matplotlib, pandas, NLTK, scikit-learn, and popular deep learning tools like TensorFlow, PyTorch, and OpenAI Gym.

Master how to set up a deep learning development environment across Windows, Linux, and Mac, including virtual machines, and installing numpy, scipy, matplotlib, ipython, pandas, Theano, and TensorFlow.