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Tensorflow in Action
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ISBN10: 1617298344
ISBN13: 9781617298349
Publisher: Manning Publications
Published: Oct 18 2022
Pages: 680
Weight: 2.20
Height: 1.50 Width: 7.32 Depth: 9.21
Language: English
ISBN13: 9781617298349
Publisher: Manning Publications
Published: Oct 18 2022
Pages: 680
Weight: 2.20
Height: 1.50 Width: 7.32 Depth: 9.21
Language: English
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
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
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
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