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Applied Deep Learning with TensorFlow 2

人工智能, 英文图书, 软件  暂无评论 ⁄ 472 次阅读+
Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addi...
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MATLAB Machine Learning Recipes, 2nd Edition

人工智能, 英文图书, 软件  暂无评论 ⁄ 1,219 次阅读+
Book Description: Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Ste...
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TensorFlow for Deep Learning

人工智能, 英文图书, 软件  暂无评论 ⁄ 1,149 次阅读+
Book Description: Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Lea...
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AI·未来

人工智能, 工业技术, 软件  暂无评论 ⁄ 2,219 次阅读+
内容简介: 迎来“深度学习”这项重大技术突破后,人工智能已经从发明的年代步入了实干的年代。 现在已是未来,我们所处的时代,已经与过去完全不同。面对已经来临的、机遇与挑战并存的人工智能时代,我们必须了解人工智能,跟上人工智能发展的脚步,这样才能不被时代淘汰。 全球目前人工智能发展的情况是怎样的? 全球的人工智能巨头企业有哪几家,现在它们有什么贡献?未来它们又将如何改变世界? 人工智能已经改变了世界前进的脚步,那么人工智能的发展阶段如何区分? 人工智能对社会的...
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Applied Deep Learning

人工智能, 英文图书, 软件  暂无评论 ⁄ 645 次阅读+
Book Description: Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. The next sect...
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