楼主: igs816
430 38

[书籍推荐] Deep Learning, Vol. 2: From Basics to Practice [推广有奖]

大师

50%

还不是VIP/贵宾

-

威望
7
论坛币
1321655 个
学术水平
2190 点
热心指数
2585 点
信用等级
1885 点
经验
378502 点
帖子
3853
精华
52
在线时间
2103 小时
注册时间
2007-8-6
最后登录
2018-5-26

楼主
igs816 在职认证  发表于 2018-5-16 16:40:09 |只看作者 |倒序
gAvEeQw1qiCxO3RR91oF3ThfP8nwI3HY.jpg

English | 19 Feb. 2018 | ASIN: B079Y1M81K | 914 Pages | PDF
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 Table of 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.

Table of Contents:
– Volume 2 –
20 Deep Learning
21 Convolutional Neural Nets (CNNs)
22 Recurrent Nerual Nets (RNNs)
23 Keras Part 1
24 Keras Part 2
25 Autoencoders
26 Reinforcement Learning
27 Generative Adversarial Networks (GANs)
28 Creative Applications
29 Datasets
30 Glossary

本帖隐藏的内容

Deep Learning, Vol. 2 From Basics to Practice.pdf (45.26 MB, 售价: 10 个论坛币)



本帖被以下文库推荐

stata SPSS
沙发
dxystata 发表于 2018-5-16 16:59:54 |只看作者
谢谢分享!
藤椅
So橘子 发表于 2018-5-16 17:02:18 |只看作者
感谢分享
板凳
fengyg 企业认证  发表于 2018-5-16 17:02:47 |只看作者
kankan
报纸
hylpy1 在职认证  发表于 2018-5-16 18:50:17 |只看作者
感谢分享
地板
elephann 发表于 2018-5-16 18:58:22 |只看作者
7
kaiwu 发表于 2018-5-16 20:19:42 |只看作者
thanks
8
shgby 发表于 2018-5-16 20:45:23 来自手机 |只看作者
Deep Learning, Vol. 2: From Basics to Practice
9
BetterT 发表于 2018-5-16 20:49:18 |只看作者
感谢楼主分享!
10
jianyu1118 在职认证  发表于 2018-5-16 20:51:53 |只看作者
kan kan!
您需要登录后才可以回帖 登录 | 我要注册

GMT+8, 2018-5-28 01:50
北京快三开奖结果北京 天津快乐十分一定牛 3d走势图 快乐8彩票 秒速时时彩官网
福建36选7开奖结果 江苏快三开奖结果啊 买赛车 江西时时彩开奖视频 江苏时时彩开奖号码
终极极速赛车 深圳风采投注 利信娱乐最新登陆网址 双色球143期 辽宁11选5走势
幸运农场在线计划手机 巴黎人娱乐城 2017pk10三把必中方法 篮球比分表 黑龙江时时彩20选8