Modern Natural Language Processing in Python

Explore how convolutional neural networks, originally for images, apply to nlp tasks like sentiment analysis and spam detection; learn cnn origins, architecture, and implementation for a Twitter sentiment analyzer.

Discover how convolutional neural networks, originally built for images, detect features with convolution, generate feature maps, pool and flatten, and adapt them for NLP text classification.

Transform text into a matrix by representing words as embeddings. Learn embeddings with a skip-gram and context matrix for CNN-ready NLP.

Explore how CNNs apply to text, using one-dimensional convolutions with filters spanning embedding dimensions, max pooling, and multi-size filters to build a sentiment analyzer.

Explore applying convolutional neural networks to natural language processing by building a sentiment analyzer that classifies tweets as positive or negative, with data preprocessing and convolutional neural network architecture.

Import dependencies for natural language processing in Python, including numpy as np, BeautifulSoup for text handling, and Google Drive integration in Colab.

Mount your Google Drive and load the train and test CSV data for sentiment analysis. Configure latin-1 encoding and the Python engine, then inspect the first rows to prepare data.

Clean and preprocess the data by dropping columns, converting text to strings, removing mentions and URLs with regex, preserving letters and punctuation, and encoding sentiment as 0/1 for model input.

Tokenize a tweet corpus with a TensorFlow datasets text encoder, convert sentences to numeric sequences, pad to a max length, and create train and test sets with random indices.

Build a deep convolutional neural network for NLP using an embedding layer, three 1d convolutional filters of sizes 2, 3, and 4, with max pooling, concatenation, and a dense classifier.

Configure the natural language processing model by setting vocab size from the tokenizer, embedding 200, filters 100, feedforward 256, dropout 0.2, batch 32, epochs 5, then create and train dqn.

Define and compile a cnn for nlp with vocab size, embedding, filters, dropout, and dense units. Train with binary or sparse cross-entropy using adam and accuracy.

Evaluate a CNN model on unseen data to measure accuracy, explore sentiment analysis with simple sentences, and discuss dataset fit and task alignment in NLP with Python.

Explore how the transformer enables sequence-to-sequence NLP tasks like translation, summarization, and chat bots using encoder-decoder architectures, attention, and embeddings.

Explore the origins of sequence-to-sequence models using encoder-decoder rnns, embeddings, and attention. See how hidden states and context vectors guide decoding before the transformer era.

Learn how the transformer replaces RNNs by using self-attention to encode and decode whole sequences. Understand the encoder-decoder architecture and how attention enables global information flow across the input.

Explore how the attention mechanism computes relations between sequence elements using dot products and softmax, then recombines them into representations. See how multi-head attention and lookahead masks enable transformer translation.

Explain how positional encoding inserts sine and cosine-based position signals into word embeddings to preserve word order in transformer encoders and decoders, addressing global attention limitations.

Explore how transformer feedforward sublayers use two dense layers with relu activation per position, and how add and norm, residuals, dropout, and positional encoding aid training.

Explore building a transformer translator from data preprocessing through embedding, positional encoding, and scaled dot-product attention to the encoder and decoder, then train and evaluate with the Europarl English–French corpus.

Import numpy and a string-processing library for preprocessing, plus time and Colab utilities to load data from drive. Configure TensorFlow 2.x, import tf and tf.keras, then use tfds tokenizer for preprocessing.

Mount Google Drive in Colab, grant access, and load English and French corpora by opening files with UTF eight encoding, then store them as europarl_english and non_breaking_prefixes.

Clean and prepare english and french corpora by removing nonbreaking prefixes, normalizing punctuation with three sharp signs, and using regex to distinguish non-ending dots from sentence terminals.

Tokenize english and french corpora with tfds texts encoder, set vocabulary sizes, and add start and end tokens. Encode sentences into input and output sequences for model training.

Set a maximum sentence length of 20 words to reduce memory use and training time, using enumerate and the Del function to remove long sentences from inputs and outputs.

Pad inputs and outputs to equal length with tf.keras.preprocessing.sequence.pad_sequences using zero padding and masking for attention. Then create shuffled, batched datasets with caching and prefetch for fast translator training.

Explore building a positional encoding layer for a transformer translator in Python, implementing sine and cosine encodings, shaping inputs, and adding them to embeddings in TensorFlow.

Implement scaled dot product attention using queries, keys, and values with look ahead and padding masks. Apply softmax to obtain attention weights for multi-head attention and combine with values.

Demonstrate multi-head attention in a transformer by projecting queries, keys, and values, splitting into subspaces, applying scaled dot-product attention per head, and concatenating with a final linear projection.

Learn to build a transformer encoder by embedding inputs, adding positional encoding, stacking encoder layers with multi-head attention, add and norm, and dense feedforward networks with dropout for training.

Explore the transformer decoder architecture, including self-attention, encoder-decoder attention, and feed-forward layers, with embedding, positional encoding, dropout, and normalization.

Build a transformer by assembling encoder and decoder, implement embeddings, padding and look-ahead masking for multi-head attention, and finalize with a dense output layer.

Learn to train a transformer by defining hyperparameters, masking padding tokens, implementing a custom learning-rate schedule, and managing checkpoints during an epoch-based training loop.

Evaluate your translator by testing a trained transformer, encoding inputs, iterating with the decoder, and decoding predictions into translations while checking end tokens and masking issues.