Tree-Based Convolutional Neural Networks，Lili Mou • Zhi Jin。2018年新书，介绍基于树构造的卷积神经网络。

Neural Networks: Tricks of the Trade, Second Edition Editors: Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller 有关神经网络、深度学习Tricks的入门经典书籍。

神经网路机器人机构及非线性系统控制，介绍了控制器的设计思路与稳定性证明，还是很详细的，推荐一下

A Guide to Convolutional Neural Networks for Computer Vision-Morgan & Claypool(2018).pdf

Neural Network Design (2nd Edition)为大牛Martin T. Hagan巨作，深入浅出地讲解了深度学习的所有算法原理，非常适合全面深入了解人工神经网络及深度学习

Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks. This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search. Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.

Neural Networks and Learning Machines (3rd Edition).pdf 这本是全英文的文字版资源。大家如果学习machine learning 的话，建议自己看英文的，毕竟这东西国外比国内要先进得多，不能让英语成为障碍。而且，原版的东西绝对比翻译的要准确些，无论翻译的水平有多高。

2017 Deep Learning and Convolutional Neural Networks for Medical Image Computing.pdf

The state-of-the-art methods used for relation classification are primarily based on statistical machine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language processing (NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the performance of these systems.

使用python一步一步搭建起自己的神经网络，对理解神经网络原理大有裨益，强烈推荐！