nwpuhtxy2010的博客

Implement neural networks with Keras on Theano and TensorFlow The book presents more than 20 working deep neural networks coded in Python using Keras, a modular neural network library that runs on top of either Google's TensorFlow or Lisa Lab's Theano backends. The reader is introduced step by step to supervised learning algorithms such as simple linear regression, classical multilayer perceptron, and more sophisticated deep convolutional networks and generative adversarial networks. In addition, the book covers unsupervised learning algorithms such as autoencoders and generative networks. Recurrent networks and long short-term memory (LSTM) networks are also explained in detail. The book goes on to cover the Keras functional API and how to customize Keras in case the reader's use case is not covered by Keras's extensive functionality. It also looks at larger, more complex systems composed of the building blocks covered previously. The book concludes with an introduction to deep reinforcement learning and how it can be used to build game playing AIs. Practical applications include code for the classification of news articles into predefined categories, syntactic analysis of texts, sentiment analysis, synthetic generation of texts, and parts of speech annotation. Image processing is also explored, with recognition of handwritten digit images, classification of images into different categories, and advanced object recognition with related image annotations. An example of identification of salient points for face detection will be also provided. Sound analysis comprises recognition of discrete speeches from multiple speakers. Reinforcement learning is used to build a deep Q-learning network capable of playing games autonomously. Experiments are the essence of the book. Each net is augmented by multiple variants that progressively improve the learning performance by changing the input parameters, the shape of the network, loss functions, and algorithms used for optimizations. Several comparisons between training on CPUs and GPUs are also provided. 全书8章310页，主要聚焦于keras api的使用，推荐给炼丹的同学

Overview： You will find the book chapters on the left hand menu You will find navigation within a section of a chapter (one webpage) on the righthand menu Full documentation on how to build this site locally are on GitHub at github.com/professorkazarinoff/Problem-Solving-with-Python/website Motivation： The motivation for writing this book is that many undergraduate engineering students have to take a programming course based on MATLAB. MATLAB is a great piece of software, but it currently costs $49.00 for a student license and requires a site license to be used on school computers. Subsequently, it is costly for a student to use MATLAB and it is costly for a college to support a course that uses MATLAB. In addition, this site license expires eventually and students need to purchase another copy often before they finish their degree. The Python programming language, on the other hand, is open source and free. To download and use Python, the cost to both the student and the college is zero (minus time spent). By moving an undergraduate engineering programming class to Python, students will save money and have greater access to the software they use in class. Further in their engineering education, students can continue to use Python for free. 全书共计13章，326页，专注于解决使用Python过程中常见的各种问题，既包括Python本身的问题，也包括常用的各种工具箱的使用问题，非常实用，强烈推荐！

机器学习是当前解决很多人工智能问题的核心技术，深度学习的出现带来了自2012 年以来的人工智 能复兴。本书是机器学习和深度学习领域的人门与提高教材，系统、深入地讲述机器学习与深度学习的主 流方法与理论，并紧密结合工程实践与应用。全书由21 章组成，共分为三大部分。第1 ～ 3 章为第一部 分，介绍机器学习的基本原理、所帘的数学知识（包括微积分、线性代数、概率论和最优化方法）， 以及机器 学习中的核心概念。第4 ～ 20 章为第二部分，是本书的主体，介绍各种常用的有监督学习算法、无监督学 习算法、半监督学习算法和强化学习算法。对于每种算法，从原理与推导、工程实现和实际应用3 个方面 进行介绍，对于大多数弈，法，都配有实验程序。第2 1 章为第三部分，介绍机苦苦学习和深度学习算法实际应 用时面11伍的问题，并给出典型的解决方案。此外，附录A 给出各种机器学习算法的总结， 附录B 给出梯度 下降法的演化关系，附录C 给出EM 算法的推导。 本书理论推导与证明详细、深入，结构清晰，详细地调述主要算法的工程实现细节，配以著名开源库的 源代码分析（包括lib svm 、liblin ear 、O pe n CV 、Ca ff e 等开源库），让读者不仅知其然，还知其所以然， 真正理解 算法、学会使用算法。对于计算－ 机、人工智能及相关专业的本科生和研究生，这是一本适合人门与系统学 习的教材，对于从事人工智能和机器学习产品研发的工程技术人员，本书也具有很强的参考价值。

Graph Algorithms by Mark Needham and Amy E. Hodler Copyright © 2019 Amy Hodler and Mark Needham. All rights reserved. What’s in This Book This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark™ or Neo4j. Although our algorithm examples utilize the Spark and Neo4j platforms, this book will also be helpful for understanding more general graph concepts, regardless of your choice of graph technologies. The first two chapters provide an introduction to graph analytics, algorithms, and theory. The third chapter briefly covers the platforms used in this book before we dive into three chapters focusing on classic graph algorithms: pathfinding, centrality, and community detection. We wrap up the book with two chapters showing how graph algorithms are used within workflows: one for general analysis and one for machine learning. At the beginning of each category of algorithms, there is a reference table to help you quickly jump to the relevant algorithm. For each algorithm, you’ll find: • An explanation of what the algorithm does • Use cases for the algorithm and references to where you can learn more • Example code providing concrete ways to use the algorithm in Spark, Neo4j, or both 图方法方面最新的参考书，本文理论实践兼备（看标题就知道了），内容高清无码书签完整诚不我欺，强烈推荐给需要的朋友！

Christoph Molnar最新上线的电子书Interpretable Machine Learning+高清无码+无水印，主要是关于机器学习可解释性方面的论述，全书二百多页，很细致，对于理解深度学习黑盒很有帮助，推荐给喜欢的朋友！

统计信号处理基础：估计与检测理论 ，本书是统计信号处理的经典外文教程的中文引进版本，搞通信的朋友都知道，全文分为两卷，卷一是估计理论，卷二是检测理论，全文九百多页，够硬够系统够详细，强烈推荐！

数字图像处理的MATLAB实现（第二版）+书签完整+文字可编辑+高清无水印+长期归档，本文质量上乘，打开有喜，非常适合做数字图像处理的朋友，强烈推荐！！

特征提取与图像处理+中文版+第二版+高清扫描+完整版，本文当前有英文版第三版，但是中文版只有这个第二版，本文高清扫描，文字图片清晰，质量不错，推荐给需要的朋友！

经典教材数字图像处理的中文版第三版，本文为高清扫描版，文字图片清晰，推荐给准备做数字图像处理的朋友！

Group and Crowd Behavior for Computer Vision[Book-2017]+高清无码+文字可复制+PDF/A长期归档 主要包括三个部分17个章节，是2017年最新出品的关于Group和Crowd Collectiveness，以及metric learning，detecting，tracking，segmenting and so on诸多方面的书，非常适合关注于人群场景理解和行为分析方面的炼金术师！强烈推荐！

本书是Peter Flach 2017年推出的机器学习的新书，本书高清无码扫描，附带完整标签，文字均可编辑复制，堪称完美！非常适合想学习机器学习相关理论的朋友，本文很有深度！！强烈推荐！！

本文是Yoshua Bengio的关于基于梯度训练深度学习框架的实战建议和思考，推荐给对此感兴趣的朋友！

本书是Nick McClure在2017年推出的新书，依赖于Tensorflow环境，通过诸多的示例，详细介绍了各种函数和模型的搭建方法，非常适合想学习Tensorflow和深度学习的朋友，本书无码高清扫描，附带完整标签，文字代码均可复制编辑，并且已归档为PDF/A格式可以长期保存，另外本书在github上有代码可供学习！堪称完美，强烈推荐！

本书是Aurélien Géron 2017年推出的新书，流行于各大深度学习网站，本书无码高清扫描，附带完整标签，文字代码示例均可复制编辑，并可长期归档PDF/A，堪称完美，非常适合想学习Tensorflow和SL库的朋友！强烈推荐！

本书是大牛Joseph Howse关于openCV在Python下应用的经典教程，本书是2013年发布第一版，但是并不落伍，技术仍然是刚刚的。本文档无码高清扫描，文字可编辑复制，附带完整书签，以保存为PDF/A格式，可供长期归档，完美推荐！

本书是Joe Minichino和Joseph Howse 2015年发布的第二版书，全书高清扫描，文字可复制编辑，附带完整标签，完美满足你的一起需求！是想学习深度学习和OpenCV3库的不二选择！强烈推荐！

本书是Luis Pedro Coelho和Willi Richert2015年五月发布的第二版新书，全书高清扫描，无水印，待完整标签，可复制编辑，完美满足你的一起需求，本书特别适合需要使用机器学习和Python工作的人，强烈推荐！

427页完整版机器学习基础，第二版，带标签，长期保存PDF/A格式，非常适合想学机器学习的朋友！

最新发布的增强学习基础介绍，是想学习增强学习的入门和经典教程，书很早就发布了，至今才完成并交付印刷，加入了最新的Alpha Zero等前沿内容，非常值得选择！

Canonical correlation analysis（CCA）典型相关分析，属于多元线性回归分析的一种，用于处理multi-view data，本文详细介绍了CCA相关的技术，是一篇综述文章，非常适合需要的朋友

LS-GAN， GAN的最新研究进展，正则化的GAN，GLS_GAN统一了WGAN，非常值得推荐

图形嵌入和非线性降维，全文108页，内容清晰翔实，带标签，非常适合对数据降维、流形学习等感兴趣的朋友

流形学习的理论和应用，全书322页，内容翔实，非常详细，高清扫描带标签版，非常适合对流形学习感兴趣的朋友

多源信息融合（第二版）清华大学出版社出版，韩崇昭等人著作，本书为高清带书签版，质量上乘，印刷清晰，值得推荐

AutoCAD制作

C&C++语言参考手册.chm，开发c语言的必备手册

高比冲双组元液体远地点火箭发动机研究高比冲双组元液体远地点火箭发动机研究，

动能拦截器的固体推进剂轨控和姿控系统

创新的推进系统