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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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.

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.

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.

Caption Generation Using PyTorch

Project

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

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