HomeCoursesNatural Language Processing with TensorFlow

Intermediate

15h

Updated 1 month ago

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.

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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.

Advanced Word Vector Algorithms

13 Lessons

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

Sentence Classification with Convolutional Neural Networks

13 Lessons

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

Premium

11.

Image Captioning with Transformers

10 Lessons

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

12.

Final Remarks

1 Lessons

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.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Packt

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