Deep Learning, Vol. 2: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (914 of 1750) Format: PDF Publication Date: February 19, 2018 Language: English ASIN: B079Y1M81K People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more. This friendly, informal book puts those tools into your pocket.

This is one of my most favorite books that teache deep learning from the very basic to advance level. The book does not emphasize on mathematics but explains the logic behind each deep learning algorithm in plain English. Even if you are the new guy in deep learning or you are already at an advance level, this book is for you. Vol 2 will be coming shortly

Advanced Software Testing - Vol. 2

3D模型 [插件] UMA Face Pack Vol. 1 version 2_人物面部表情插件，链接：https://pan.baidu.com/s/1rMwRpnQUTQZEvMlCDRX29A，购买之前请先确认链接可用，文件里是链接密码。资源仅供大家学习交流，下载的文件很小，是因为这是网盘的资源链接的密码，资源在网盘中，请大家确认链接有效后，放心下载。

Digital Signal Processing with Matlab Examples, Vol. 2：Decomposition, Recovery, Data-Based Actions，2017.(944s).pdf

Geometric Algebra Applications Vol. I: Computer Vision, Graphics and Neurocomputing By 作者: Eduardo Bayro-Corrochano ISBN-10 书号: 3319748289 ISBN-13 书号: 9783319748283 Edition 版本: 1st ed. 2019 出版日期: 2018-08-13 pages 页数: 753 $219.99 The goal of the Volume I Geometric Algebra for Computer Vision, Graphics and Neural Computing is to present a unified mathematical treatment of diverse problems in the general domain of artificial intelligence and associated fields using Clifford, or geometric, algebra. Geometric algebra provides a rich and general mathematical framework for Geometric Cybernetics in order to develop solutions, concepts and computer algorithms without losing geometric insight of the problem in question. Current mathematical subjects can be treated in an unified manner without abandoning the mathematical system of geometric algebra for instance: multilinear algebra, projective and affine geometry, calculus on manifolds, Riemann geometry, the representation of Lie algebras and Lie groups using bivector algebras and conformal geometry. By treating a wide spectrum of problems in a common language, this Volume I offers both new insights and new solutions that should be useful to scientists, and engineers working in different areas related with the development and building of intelligent machines. Each chapter is written in accessible terms accompanied by numerous examples, figures and a complementary appendix on Clifford algebras, all to clarify the theory and the crucial aspects of the application of geometric algebra to problems in graphics engineering, image processing, pattern recognition, computer vision, machine learning, neural computing and cognitive systems. 1 Geometric Algebra for the Twenty-First Century Cybernetics Part I Fundamentals of Geometric Algebra 2Introduction to Geometric Algebra 3 Differentiation,Linear,and Multilinear Functions in Geometric Algebra 4 Geometric Calculus 5 Lie Algebras,Lie Groups,and Algebra of Incidence Part ll Euclidean,Pseudo-Euclidean Geometric Algebras, Incidence Algebra,Conformal and Projective Geometric Algebras 62D,3D,and 4D Geometric Algebras 7 Kinematics of the 2D and 3D Spaces 8 Conformal Geometric Algebra 9 The Geometric Algebras G6,0,2+,G6,3,G9,3+,G6,0,6+ 10 Programming Issues Part ll Image Processing and Computer Vision 11 Quaternion-Clifford Fourier and Wavelet Transforms 12 Geometric Algebra of Computer Vision Part IV Machine Learning 13 Geometric Neurocomputing Part V Applications of GA in lmage Processing,Graphics and Computer Vision 14 Applications of Lie Filters,Quaternion Fourier,and Wavelet Transforms 15 Invariants Theory in Computer Vision and Omnidirectional Vision 16 Geometric Algebra Tensor Voting,Hough Transform,Voting and Perception Using Conformal Geometric Algebra 17 Modeling and Registration of Medical Data Part VI Applications of GA in Machine Learning 18 Applications in Neurocomputing 19 Neurocomputing for 2D Contour and 3D Surface Reconstruction 20 Clifford Algebras and Related Algebras

Sanet.st_Deep_Learning,_Vol._2_From_Basics_to_Practice_-_Andrew_Glassner_带书签.pdf

The goal of the Volume I Geometric Algebra for Computer Vision, Graphics and Neural Computing is to present a unified mathematical treatment of diverse problems in the general domain of artificial intelligence and associated fields using Clifford, or geometric, algebra. Geometric algebra provides a rich and general mathematical framework for Geometric Cybernetics in order to develop solutions, concepts and computer algorithms without losing geometric insight of the problem in question. Current mathematical subjects can be treated in an unified manner without abandoning the mathematical system of geometric algebra for instance: multilinear algebra, projective and affine geometry, calculus on manifolds, Riemann geometry, the representation of Lie algebras and Lie groups using bivector algebras and conformal geometry. By treating a wide spectrum of problems in a common language, this Volume I offers both new insights and new solutions that should be useful to scientists, and engineers working in different areas related with the development and building of intelligent machines. Each chapter is written in accessible terms accompanied by numerous examples, figures and a complementary appendix on Clifford algebras, all to clarify the theory and the crucial aspects of the application of geometric algebra to problems in graphics engineering, image processing, pattern recognition, computer vision, machine learning, neural computing and cognitive systems.

在本文中，我们提出了一种非线性Tomlinson-Harashima预编码（THP）方案，用于直接检测双边带（DSB）PAM-4传输系统中的非线性失真抑制。 在传统THP的基础上，通过引入非线性分量来修改反馈项。 这样，可以获得更准确的反馈，以减轻信号失真，尤其是非线性失真，包括由色散和平方律检测引起的信号-信号跳动干扰和非线性幂级数。 同时，为了降低计算复杂度，我们还建议仅在非线性THP中保留具有相邻抽头乘积的非线性核。 为了验证有效性，实验证明了双边边带（DSB）PAM-4信号在1550nm窗口中的传输。 接收器侧采用Volterra FFE来抑制线性和非线性前体。 我们优化了各种硬件参数，并对非线性THP内核进行了适当的简化。 结果表明，与传统的线性THP相比，提出的非线性THP最多可将BER降低三倍。 最后，结合提出的非线性THP和常规的Volterra FFE，我们在硬决策前向纠错（HD-FEC）阈值下分别成功地在80 km和40 km上分别传输了84 Gbps PAM-4和107 Gbps PAM-4为3.8×10-3。

集成电路工艺：The Submicron MOSFET An understanding of device physics has become even more important that MOSFETs have crossed the long-channnel frontier into the submicron realm.