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Deep Learning with TensorFlow and Keras Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3e [#473109]
English | 2022 | ISBN: 1803232919 | 699 pages | True PDF | 25.5 MB
Build cutting edge machine and deep learning systems for the lab, production, and mobile devices
Key Features
Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniques
Book Description
Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.
TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.
This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
What you will learn
Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras API
Who this book is for
This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.
Some machine learning knowledge would be useful. We don't assume TF knowledge.
Table of Contents
Neural Networks Foundations with TFRegression and ClassificationConvolutional Neural NetworksWord EmbeddingsRecurrent Neural NetworkTransformersUnsupervised LearningAutoencodersGenerative ModelsSelf-Supervised LearningReinforcement LearningProbabilistic TensorFlowAn Introduction to AutoMLThe Math Behind Deep LearningTensor Processing UnitOther Useful Deep Learning LibrariesGraph Neural NetworksMachine Learning Best PracticesTensorFlow 2 EcosystemAdvanced Convolutional Neural Networks
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