Unlock the TensorFlow design secrets behind successful deep learning applications! Deep learning StackOverflow contributor Thushan Ganegedara teaches you the new features of TensorFlow 2 in this hands-on guide.

In TensorFlow in Action you will learn:

Fundamentals of TensorFlow

Implementing deep learning networks

Picking a high-level Keras API for model building with confidence

Writing comprehensive end-to-end data pipelines

Building models for computer vision and natural language processing

Utilizing pretrained NLP models

Recent algorithms including transformers, attention models, and ElMo

In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts otherwise trapped in dense academic papers. As you dive into modern deep learning techniques like transformer and attention models, you’ll benefit from the unique insights of a top StackOverflow contributor for deep learning and NLP.

About the technology

Google’s TensorFlow framework sits at the heart of modern deep learning. Boasting practical features like multi-GPU support, network data visualization, and easy production pipelines using TensorFlow Extended (TFX), TensorFlow provides the most efficient path to professional AI applications. And the Keras library, fully integrated into TensorFlow 2, makes it a snap to build and train even complex models for vision, language, and more.

About the book

TensorFlow in Action teaches you to construct, train, and deploy deep learning models using TensorFlow 2. In this practical tutorial, you’ll build reusable skill hands-on as you create production-ready applications such as a French-to-English translator and a neural network that can write fiction. You’ll appreciate the in-depth explanations that go from DL basics to advanced applications in NLP, image processing, and MLOps, complete with important details that you’ll return to reference over and over.

What's inside

Covers TensorFlow 2.9

Recent algorithms including transformers, attention models, and ElMo

Build on pretrained models

Writing end-to-end data pipelines with TFX

About the reader

For Python programmers with basic deep learning skills.

About the author

Thushan Ganegedara is a senior ML engineer at Canva and TensorFlow expert. He holds a PhD in machine learning from the University of Sydney.

Table of Contents

PART 1 FOUNDATIONS OF TENSORFLOW 2 AND DEEP LEARNING

1 The amazing world of TensorFlow

2 TensorFlow 2

3 Keras and data retrieval in TensorFlow 2

4 Dipping toes in deep learning

5 State-of-the-art in deep learning: Transformers

PART 2 LOOK MA, NO HANDS! DEEP NETWORKS IN THE REAL WORLD

6 Teaching machines to see: Image classification with CNNs

7 Teaching machines to see better: Improving CNNs and making them confess

8 Telling things apart: Image segmentation

9 Natural language processing with TensorFlow: Sentiment analysis

10 Natural language processing with TensorFlow: Language modeling

PART 3 ADVANCED DEEP NETWORKS FOR COMPLEX PROBLEMS

11 Sequence-to-sequence learning: Part 1

12 Sequence-to-sequence learning: Part 2

13 Transformers

14 TensorBoard: Big brother of TensorFlow

15 TFX: MLOps and deploying models with TensorFlow