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Natural Language Processing with TensorFlow
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1.
Introduction to Natural Language Processing
Introduction: Natural Language Processing
What Is Natural Language Processing?
Tasks of Natural Language Processing
The Traditional Approach to Natural Language Processing
The Deep Learning Approach to Natural Language Processing
Summary: Introduction to Natural Language Processing
2.
Understanding TensorFlow 2
Introduction: Understanding TensorFlow 2
What Is TensorFlow 2?
TensorFlow 2 Architecture: Building and Executing Graphs
Café Le TensorFlow 2: Understanding TensorFlow 2 with an Analogy
Flashback: TensorFlow 1
Defining Inputs in TensorFlow
Building a Data Pipeline Using the tf.data API
Defining Variables and Output in TensorFlow
Defining Operations in TensorFlow
Neural Network-Related Operations
Keras: TensorFlow's Model-Building API
Implementing Our First Neural Network
Summary: Understanding TensorFlow 2
Quiz: Understanding TensorFlow 2
3.
Word2vec: Learning Word Embeddings
Introduction of Word2vec: Learning Word Embeddings
Classical Approaches to Learning Word Representations
An Intuitive Understanding of Word2vec
The Skip-Gram Algorithm
Data Generators in the Skip-Gram Algorithm with TensorFlow
Implementing the Skip-Gram Architecture with TensorFlow
Training and Evaluating the Skip-Gram Model
The Continuous Bag-of-Words Algorithm
Summary of Word2vec: Learning Word Embeddings
Quiz of Word2vec: Learning Word Embeddings
4.
Advanced Word Vector Algorithms
Introduction: Advanced Word Vector Algorithms
GloVe: Global Vectors Representation
Implementing GloVe
Generating Data for GloVe
Training and Evaluating GloVe
ELMo: Taking Ambiguities Out of Word Vectors
Preparing Inputs for ELMo
Generating Embeddings with ELMo
Dataset in Document Classification with ELMo
Generating Document Embeddings
Classifying Documents with Document Embeddings
Summary: Advanced Word Vector Algorithms
Quiz: Advanced Word Vector Algorithms
5.
Sentence Classification with Convolutional Neural Networks
Introduction: Sentence Classification with CNNs
Introducing CNNs
Understanding CNNs: Convolution Operations
Understanding CNNs: Pooling Operations
Understanding CNNs: Fully Connected Layers
Exercise: Image Classification on Fashion-MNIST with CNN
CNNs for Sentence Classification: Transformation of Data
CNNs for Sentence Classification: Downloading and Preparing Data
CNNs for Sentence Classification: Building a Tokenizer
The Sentence Classification CNN Model
Sentence Classification with CNNs
Summary: Sentence Classification with CNNs
Quiz: Sentence Classification with CNNs
6.
Recurrent Neural Networks
Introduction: Recurrent Neural Networks
Understanding RNNs
Backpropagation through Time
Applications of RNNs
Named Entity Recognition with RNNs: Preparing Data
Named Entity Recognition with RNNs: Defining the Model
Named Entity Recognition with RNNs: Training and Evaluation
NER with Character and Token Embeddings
Summary: Recurrent Neural Networks
Quiz: Recurrent Neural Networks
7.
Understanding Long Short-Term Memory Networks
Introduction: Understanding Long Short-Term Memory Networks
Understanding Long Short-Term Memory Networks
How LSTMs Solve the Vanishing Gradient Problem
Improving LSTMs
Other Variants of LSTMs
Summary: Understanding Long Short-Term Memory Networks
Quiz: Understanding Long Short-Term Memory Networks
8.
Applications of LSTM: Generating Text
Introduction: Applications of LSTMs—Generating Text
Understanding the Data
Implementing the Language Model
Generating New Text with the Model
Comparing LSTMs to LSTMs with Peephole Connections and GRUs
Improving Sequential Models: Beam Search
Improving LSTMs: Generating Text with Words Instead of N-grams
Summary: Applications of LSTMs—Generating Text
Quiz: Applications of LSTMs—Generating Text
9.
Sequence-to-Sequence Learning: Neural Machine Translation
Introduction: Sequence-to-Sequence Learning—NMT
Machine Translation
Understanding Neural Machine Translation
Preparing Data for the NMT System
Defining the NMT Model
Attention: Analyzing, Computing, and Implementing
Training the NMT
The BLEU Score: Evaluating Machine Translation Systems
Visualizing Attention Patterns
Inference with NMT
Other Applications of Seq2Seq Models: Chatbots
Summary: Sequence-to-Sequence Learning—NMT
Quiz: Sequence-to-Sequence Learning—NMT
10.
Transformers
Introduction: Transformers
Transformer Architecture: Encoder, Decoder, and Computing Output
Transformer Architecture: Embedding Layers
Transformer Architecture: Residuals and Normalization
Understanding BERT
Use Case: Using BERT to Answer Questions
Use Case: Implementing BERT
Training and Evaluating Model and Answering Questions with BERT
Summary: Transformers
Quiz: Transformers
Project
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Sarcasm Classification Using BERT
11.
Image Captioning with Transformers
Introduction: Image Captioning with Transformers
Getting to Know the Data
Processing and Tokenizing Data
Defining a tf.data.Dataset
The Machine Learning Pipeline for Image Caption Generation
Implementing and Training the Model with TensorFlow
Evaluating the Results Quantitatively
Evaluating the Model and Generating Captions from It
Summary: Image Captioning with Transformers
Quiz: Image Captioning with Transformers
12.
Final Remarks
Wrap Up
13.
Appendix: Mathematical Foundations and Advanced TensorFlow
Introduction to the Technical Tools
Basic Data Structures
Special Types of Matrices
Probability
Visualizing Word Embeddings with TensorBoard
Summary: Appendix
Mock Interview
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Fundamentals of NLP
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Natural Language Processing with TensorFlow
Quiz: Understanding TensorFlow 2
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