这是基于Pytorch的图片风格迁移教程源码，对其进行了逐句对照解析以便于理解。 详情请搜索博文：【Pytorch代码】神经风格迁移Pytorch教程代码 逐句解析

机器学习，神经网络多层感知器实现，稍事修改即可实现手写数字识别，鸢尾花识别实验等

Exploring an advanced state of the art deep learning models and its applications using Popular python libraries like Keras, Tensorflow, and Pytorch Key Features • A strong foundation on neural networks and deep learning with Python libraries. • Explore advanced deep learning techniques and their applications across computer vision and NLP. • Learn how a computer can navigate in complex environments with reinforcement learning. Book Description With the surge of Artificial Intelligence in each and every application catering to both business and consumer needs, Deep Learning becomes the prime need of today and future market demands. This book explores deep learning and builds a strong deep learning mindset in order to put them into use in their smart artificial intelligence projects. This second edition builds strong grounds of deep learning, deep neural networks and how to train them with high-performance algorithms and popular python frameworks. You will uncover different neural networks architectures like convolutional networks, recurrent networks, long short term memory (LSTM) and solve problems across image recognition, natural language processing, and time-series prediction. You will also explore the newly evolved area of reinforcement learning and it will help you to understand the state-of-the-art algorithms which are the main engines behind popular game Go, Atari, and Dota. By the end of the book, you will be well versed with practical deep learning knowledge and its real-world applications What you will learn • Grasp mathematical theory behind neural networks and deep learning process. • Investigate and resolve computer vision challenges using convolutional networks and capsule networks. • Solve Generative tasks using Variational Autoencoders and Generative Adversarial Nets (GANs). • Explore Reinforcement Learning and understand how agents behave in a complex environment. • Implement complex natural language processing tasks using recurrent networks (LSTM

Neural Network Design Demonstrations(神经网络设计代码演示) 一个神经网络源程序教学任务包，里面有130个M文件，可直接调用，供大家参考。 Neural Networks: A Classroom Approach Satish kumar

principles of neural science 第4版原版，有书目可搜索

当代最经典的自适应神经网络控制书 由IEEE Fellow Shuzhi Sam Ge 编写

一部值得精读的著作,将神经网络方法和自然语言处理的相关课题紧密的联合了起来.介绍了神经网络的构建细节和机器学习的一些基本内容,并且包含了RNN,CNN等主流神经网络在NLP中的应用实例,另外2017年新书,有最新的学术信息.

Michael Nielsen的⼀本书兼顾理论和动⼿实践的书。讲解了神经网络和深度学习的众多核心概念，也包含了作者对深度学习的深刻理解和透彻思考，并附代码实例。非常适合初学者入门。

神经网络设计第二版pdf, Neural Network Design (2nd Edition)英语原版，2014年出版，Martin T. Hagan Oklahoma State University Stillwater, Oklahoma Howard B. Demuth University of Colorado Boulder, Colorado Mark Hudson Beale MHB Inc. Hayden, Idaho Orlando De Jesús Consultant Frisco, Texas

During the 1980s and early 1990s there was significant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have hardware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche areas this technology was never sufficiently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period mentioned were never large enough nor fast enough for serious artificial-neuralnetwork (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.