Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
by: Nikhil Buduma
ISBN-10: 1491925612
ISBN-13: 9781491925614
Edition 版本:‏ 1
Released: 2017-06-29
Pages: 304
9
With the reinvigoration of neural networks in the 2000s,deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field.
Large companies such as Google,Microsoft,and Facebook have taken notice,and are actively growing in-house deep learning teams. For the rest of us however,deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is,have familiarity with the Python programming language,and have some mathematical background with calculus,this book will help you get started.
Contents
Chapter 1. The Neural Network
Chapter 2. Training Feed-Forward Neural Networks
Chapter 3. Implementing Neural Networks in TensorFlow
Chapter 4. Beyond Gradient Descent
Chapter 5. Convolutional Neural Networks
Chapter 6. Embedding and Representation Learning
Chapter 7. Models for Sequence Analysis
Chapter 8. Memory Augmented Neural Networks
Chapter 9. Deep Reinforcement Learning
随着神经网络在二十世纪二十年代的振兴，深入学习已经成为一个非常活跃的研究领域，为现代机器学习铺平了道路。本书使用博览会和例子来帮助您了解这个复杂领域的主要概念。
像Google，微软和Facebook这样的大型公司已经注意到了，并且正在积极发展内部的深度学习团队。对于其他人来说，深度学习仍然是一个相当复杂和困难的对象。如果您对什么是机器学习有一个基本的了解，熟悉Python编程语言，并且有一些数学背景与微积分，这本书将帮助您开始。
目录
第一章神经网络
第二章训练前馈神经网络
第三章在TensorFlow中实现神经网络
第4章。超越渐变下降
卷积神经网络
第六章嵌入和表征学习
第七章序列分析模型
第八章内存增强神经网络
第九章深度加强学习