Advanced Natural Language Processing with TensorFlow 2

Spis treści książki

• Preface
  • Who this book is for
  • What this book covers
  • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
  • Get in touch
    • Reviews
• Essentials of NLP
  • A typical text processing workflow
  • Data collection and labeling
    • Collecting labeled data
      • Development environment setup
    • Enabling GPUs on Google Colab
  • Text normalization
    • Modeling normalized data
    • Tokenization
      • Segmentation in Japanese
      • Modeling tokenized data
    • Stop word removal
      • Modeling data with stop words removed
    • Part-of-speech tagging
      • Modeling data with POS tagging
    • Stemming and lemmatization
  • Vectorizing text
    • Count-based vectorization
      • Modeling after count-based vectorization
    • Term Frequency-Inverse Document Frequency (TF-IDF)
      • Modeling using TF-IDF features
    • Word vectors
      • Pretrained models using Word2Vec embeddings
  • Summary
• Understanding Sentiment in Natural Language with BiLSTMs
  • Natural language understanding
  • Bi-directional LSTMs BiLSTMs
    • RNN building blocks
    • Long short-term memory (LSTM) networks
    • Gated recurrent units (GRUs)
    • Sentiment classification with LSTMs
      • Loading the data
      • Normalization and vectorization
      • LSTM model with embeddings
      • BiLSTM model
  • Summary
• Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding
  • Named Entity Recognition
    • The GMB data set
  • Loading the data
  • Normalizing and vectorizing data
  • A BiLSTM model
  • Conditional random fields (CRFs)
  • NER with BiLSTM and CRFs
    • Implementing the custom CRF layer, loss, and model
      • A custom CRF model
      • A custom loss function for NER using a CRF
    • Implementing custom training
  • Viterbi decoding
    • The probability of the first word label
  • Summary
• Transfer Learning with BERT
  • Transfer learning overview
    • Types of transfer learning
      • Domain adaptation
      • Multi-task learning
      • Sequential learning
  • IMDb sentiment analysis with GloVe embeddings
    • GloVe embeddings
    • Loading IMDb training data
    • Loading pre-trained GloVe embeddings
    • Creating a pre-trained embedding matrix using GloVe
    • Feature extraction model
    • Fine-tuning model
  • BERT-based transfer learning
    • Encoder-decoder networks
    • Attention model
    • Transformer model
    • The bidirectional encoder representations from transformers (BERT) model
      • Tokenization and normalization with BERT
      • Pre-built BERT classification model
      • Custom model with BERT
  • Summary
• Generating Text with RNNs and GPT-2
  • Generating text one character at a time
    • Data loading and pre-processing
    • Data normalization and tokenization
    • Training the model
    • Implementing learning rate decay as custom callback
    • Generating text with greedy search
  • Generative Pre-Training (GPT-2) model
    • Generating text with GPT-2
  • Summary
• Text Summarization with Seq2seq Attention and Transformer Networks
  • Overview of text summarization
  • Data loading and pre-processing
  • Data tokenization and vectorization
  • Seq2seq model with attention
    • Encoder model
    • Bahdanau attention layer
    • Decoder model
  • Training the model
  • Generating summaries
    • Greedy search
    • Beam search
    • Decoding penalties with beam search
  • Evaluating summaries
  • ROUGE metric evaluation
  • Summarization state of the art
  • Summary
• Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks
  • Multi-modal deep learning
    • Vision and language tasks
  • Image captioning
  • MS-COCO dataset for image captioning
  • Image processing with CNNs and ResNet50
    • CNNs
      • Convolutions
      • Pooling
      • Regularization with dropout
      • Residual connections and ResNets
  • Image feature extraction with ResNet50
  • The Transformer model
    • Positional encoding and masks
    • Scaled dot-product and multi-head attention
    • VisualEncoder
    • Decoder
    • Transformer
  • Training the Transformer model with VisualEncoder
    • Loading training data
    • Instantiating the Transformer model
    • Custom learning rate schedule
    • Loss and metrics
    • Checkpoints and masks
    • Custom training
  • Generating captions
  • Improving performance and state-of-the-art models
  • Summary
• Weakly Supervised Learning for Classification with Snorkel
  • Weak supervision
    • Inner workings of weak supervision with labeling functions
  • Using weakly supervised labels to improve IMDb sentiment analysis
    • Pre-processing the IMDb dataset
    • Learning a subword tokenizer
    • A BiLSTM baseline model
      • Tokenization and vectorizing data
      • Training using a BiLSTM model
  • Weakly supervised labeling with Snorkel
    • Iterating on labeling functions
  • Nave-Bayes model for finding keywords
    • Evaluating weakly supervised labels on the training set
    • Generating unsupervised labels for unlabeled data
    • Training BiLSTM on weakly supervised data from Snorkel
  • Summary
• Building Conversational AI Applications with Deep Learning
  • Overview of conversational agents
    • Task-oriented or slot-filling systems
  • Question-answering and MRC conversational agents
  • General conversational agents
  • Summary
  • Epilogue
• Installation and Setup Instructions for Code
  • GitHub location
  • Chapter 1 installation instructions
  • Chapter 2 installation instructions
  • Chapter 3 installation instructions
  • Chapter 4 installation instructions
  • Chapter 5 installation instructions
  • Chapter 6 installation instructions
  • Chapter 7 installation instructions
  • Chapter 8 installation instructions
  • Chapter 9 installation instructions
• Other Books You May Enjoy
• Index

pokaż cały spis treści

ukryj recenzje