TensorFlow in Action

<b>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.</b><br><br>In <i>TensorFlow in Action</i> you will learn:<br> <br> Fundamentals of TensorFlow<br> Implementing deep learning networks<br> Picking a high-level Keras API for model building with confidence<br> Writing comprehensive end-to-end data pipelines<br> Building models for computer vision and natural language processing<br> Utilizing pretrained NLP models<br> Recent algorithms including transformers, attention models, and ElMo<br> <br> In <i>TensorFlow in Action</i>, 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.<br> <br> Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.<br> <br> About the technology<br> 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.<br> <br> About the book<br> <i>TensorFlow in Action</i> 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.<br> <br> What's inside<br> <br> Covers TensorFlow 2.9<br> Recent algorithms including transformers, attention models, and ElMo<br> Build on pretrained models<br> Writing end-to-end data pipelines with TFX<br> <br>About the reader<br> For Python programmers with basic deep learning skills.<br> <br> About the author<br> <b>Thushan Ganegedara</b> is a senior ML engineer at Canva and TensorFlow expert. He holds a PhD in machine learning from the University of Sydney.<br> <br>Table of Contents<br> PART 1 FOUNDATIONS OF TENSORFLOW 2 AND DEEP LEARNING<br> 1 The amazing world of TensorFlow<br> 2 TensorFlow 2<br> 3 Keras and data retrieval in TensorFlow 2<br> 4 Dipping toes in deep learning<br> 5 State-of-the-art in deep learning: Transformers<br> PART 2 LOOK MA, NO HANDS! DEEP NETWORKS IN THE REAL WORLD<br> 6 Teaching machines to see: Image classification with CNNs<br> 7 Teaching machines to see better: Improving CNNs and making them confess<br> 8 Telling things apart: Image segmentation<br> 9 Natural language processing with TensorFlow: Sentiment analysis<br> 10 Natural language processing with TensorFlow: Language modeling<br> PART 3 ADVANCED DEEP NETWORKS FOR COMPLEX PROBLEMS<br> 11 Sequence-to-sequence learning: Part 1<br> 12 Sequence-to-sequence learning: Part 2<br> 13 Transformers<br> 14 TensorBoard: Big brother of TensorFlow<br> 15 TFX: MLOps and deploying models with TensorFlow