Deep Learning with TensorFlow 2 and Keras - Second Edition
內容描述
Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
ntroduces and then uses TensorFlow 2 and Keras right from the start
Teaches key machine and deep learning techniques
Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
目錄大綱
Neural Network Foundations with TensorFlow 2.0
TensorFlow 1.x and 2.x
Regression
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Generative Adversarial Networks
Word Embeddings
Recurrent Neural Networks
Autoencoders
Unsupervised Learning
Reinforcement Learning
TensorFlow and Cloud
TensorFlow for Mobile and IoT and TensorFlow.js
An introduction to AutoML
The Math Behind Deep Learning
Tensor Processing Unit
作者介紹
Antonio Gulli has a passion for establishing and managing global technological talent, for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, he serves as the Engineering Director for the Office of the CTO, Google Cloud. Previously, he served as Google Warsaw Site leader doubling the size of the engineering site.
Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi, and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her master's in electronics in 1996 and her PhD in 2011. She has more than 50 publications in international journals and conferences. Her present research areas include machine learning, artificial intelligence, deep reinforcement learning, and robotics.
Sujit Pal is a technology research director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group. His areas of interest include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora. In addition to co-authoring a book on deep learning with Antonio Gulli, Sujit writes about technology on his blog, Salmon Run.
