Summary GANs in Action teaches you how to build and train your own Generative Adversarial Networks, one of the most important innovations in deep learning. In this book, you'll learn how to start building your own simple adversarial system as you explore the foundation of GAN architecture: the generator and discriminator networks. Purchase of the print book includes a free eBoo...(展开全部)

Kuntal Ganguly是一位大数据分析工程师，其利用机器学习和大数据框架来搭建大规模数据驱动系统。Kuntal具有7年的大数据和机器学习应用构建经验。 Kuntal为云上客户提供搭建实时分析系统的解决方案。这其中利用到了托管式的云服务和开源Hadoop生态系统工具，诸如Spark、Kafka、Storm、Solr以及机器学习和深度学习框架。 Kuntal也喜欢亲自动手参与软件的开发过程，并且曾经独自一人完成了多个大规模分布式应用从构思、架构、开发一直到部署的整个过程。他也是一位机器学习和深度学习的从业者，十分热衷于构建智能应用。

Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep...(展开全部)

In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep l...(展开全部)

http://neuralnetworksanddeeplearning.com/

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks wor...(展开全部)

http://neuralnetworksanddeeplearning.com/

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

This book is indented to provide a full overview of deep learning. From the beginner in deep learning and artificial intelligence, to the data scientist who wants to become familiar with Theano and its supporting libraries, or have an extended understanding of deep neural nets.Some basic skills in Python programming and computer science will help, as well as skills in elementary algebra and calculus.