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Intermediate

15h

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Natural Language Processing with TensorFlow
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Gain insights into NLP with TensorFlow and Keras, building embeddings, and mastering CNNs, RNNs, transformers for tasks like text generation, translation, and question answering with BERT.

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Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms of designing new features. Tuning models can now be efficiently solved using NLP. In this course, you will learn the fundamentals of TensorFlow and Keras, which is a Python-based interface for TensorFlow. Next, you will build embeddings and other vector representations, including the skip-gram model, continuous bag-of-words, and Global Vector representations. You will then learn about convolutional neural networks, recurrent neural networks, and long short-term memory networks. You’ll also learn to solve NLP tasks like named entity recognition, text generation, and machine translation using them. Lastly, you will learn transformer-based architectures and perform question answering (using BERT) and caption generation. By the end of this course, you will have a solid foundation in NLP and the skills to build TensorFlow-based solutions for a wide range of NLP problems.

Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms...Show More

WHAT YOU'LL LEARN

Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)

Understanding of deep models like CNNs, RNNs, LSTMs, and GRUs

Hands-on experience building NLP tools using TensorFlow, Keras, and Hugging Face libraries

Working knowledge of transformers and their variants in sequence-to-sequence modeling

Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)

Content

122 Lessons10 Quizzes

Introduction to Natural Language Processing

6 Lessons

Get familiar with NLP tasks, traditional approaches, and deep learning advancements.

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

Understanding TensorFlow 2

14 Lessons

Get started with TensorFlow 2's architecture, operations, data pipelines, and Keras API for neural networks.

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

Word2vec: Learning Word Embeddings

10 Lessons

Examine Word2vec and classical methods for word representation, with TensorFlow implementation.

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

Advanced Word Vector Algorithms

13 Lessons

Grasp the fundamentals of advanced word vector algorithms like GloVe and ELMo for NLP.

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

Sentence Classification with Convolutional Neural Networks

13 Lessons

Dive into CNNs for image and sentence classification with TensorFlow, emphasizing practical applications.

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

Recurrent Neural Networks

10 Lessons

Focus on Recurrent Neural Networks' structure, training, and applications in sequence data tasks.

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

Understanding Long Short-Term Memory Networks

7 Lessons

Build on LSTM networks to handle short-term and long-term dependencies effectively.

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

Applications of LSTM: Generating Text

9 Lessons

Learn how to use LSTMs, GRUs, and beam search for efficient text generation.

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

Sequence-to-Sequence Learning: Neural Machine Translation

13 Lessons

Discover the logic behind sequence-to-sequence learning for neural machine translation, including model architecture, data preparation, training, and evaluation.

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

10 Lessons

Master the steps to leverage transformers and BERT for NLP tasks and question answering.

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

Sarcasm Classification Using BERT

Project

11.

Image Captioning with Transformers

10 Lessons

Enhance your skills in image captioning with transformer models using deep learning techniques.

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

Caption Generation Using PyTorch

Project

12.

Final Remarks

1 Lessons

Deepen your knowledge of NLP principles, TensorFlow models, neural networks, and key algorithms.

Wrap Up

13.

Appendix: Mathematical Foundations and Advanced TensorFlow

6 Lessons

Investigate essential mathematical tools, data structures, and TensorBoard for understanding NLP with TensorFlow.

Introduction to the Technical Tools

Basic Data Structures

Special Types of Matrices

Probability

Visualizing Word Embeddings with TensorBoard

Summary: Appendix

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Packt

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