Natural Language Processing with TensorFlow

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.

Intermediate

122 Lessons

15h

Certificate of Completion

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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Explanations

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Explanations

This course includes

33 Playgrounds

10 Quizzes

This course includes

33 Playgrounds

10 Quizzes

Course Overview

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 ...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

What You'll Learn

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

Course Content

Introduction to Natural Language Processing

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

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

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

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

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.

Understanding Long Short-Term Memory Networks

7 Lessons

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

Applications of LSTM: Generating Text

9 Lessons

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

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.

10.

Transformers

10 Lessons

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

11.

Image Captioning with Transformers

10 Lessons

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

12.

Final Remarks

1 Lesson

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

13.

Appendix: Mathematical Foundations and Advanced TensorFlow

6 Lessons

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

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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