Natural Language Processing with TensorFlow

Deep learning has revolutionized NLP and problems that require a large amount of work in terms of new features. Tuning can now be efficiently solved using NLP.

Intermediate

122 Lessons

15h

Certificate of Completion

Deep learning has revolutionized NLP and problems that require a large amount of work in terms of new features. Tuning can now be efficiently solved using NLP.

AI-POWERED

Explanations

AI-POWERED

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

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

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

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

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

Recurrent Neural Networks

10 Lessons

Understanding Long Short-Term Memory Networks

7 Lessons

Applications of LSTM: Generating Text

9 Lessons

Sequence-to-Sequence Learning: Neural Machine Translation

13 Lessons

Transformers

10 Lessons

Image Captioning with Transformers

10 Lessons

Final Remarks

1 Lesson

Appendix: Mathematical Foundations and Advanced TensorFlow

6 Lessons

Course Author

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Emma Bostian 🐞

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