深度学习入门到实践：基础知识讲解

深度学习
需积分: 24 33 下载量 108 浏览量 更新于2023-05-22 2 收藏 130.43MB PDF 举报
"《深度学习 Vol 1：从基础到实践》是一本由Andrew Glassner编著的专业深度学习教材。该书旨在提供对深度学习基础知识和实践经验的全面介绍，适合初学者和进阶者深入理解这一领域的核心概念和技术。作者以其独特的视角，结合实际案例，带领读者探索深度学习的世界，包括神经网络、卷积神经网络、循环神经网络等基本原理。 书中版权信息表明，2018年出版，保留所有权利，未经作者Andrew Glassner事先书面许可，除非在学术文章或评论中引用部分短句，否则不得复制、存储或以任何形式传播，也不得在没有明确指出的情况下修改或再分发。然而，与本书相关的程序文件（在GitHub上可获取）以及书中提供的图像和图表遵循MIT许可证，允许用户自由使用，但非作者原创的图片仍受原有版权保护。 作者强调，本书中的软件提供无任何形式的担保，包括但不限于商品质量保证和特定用途适用性。这意味着读者应自行承担使用这些代码的风险，并可能需要根据自身需求进行修改和适配。 《深度学习 Vol 1：从基础到实践》不仅涵盖了理论知识，还注重实践应用，为读者提供了从理论到实战的完整路径，帮助他们掌握深度学习的基本工具和方法，以便在人工智能领域中取得突破。对于希望系统学习和提升深度学习技能的学习者来说，这是一本不可或缺的参考书籍。"

Deep Learning, Vol. 2: From Basics to Practice

2019-01-06 上传
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.

Deep Learning, Vol. 1 From Basics to Practice 无水印原版pdf

2018-05-23 上传
Deep Learning, Vol. 1 From Basics to Practice 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 查看此书详细信息请在美国亚马逊官网搜索此书

Deep Learning, Vol. 1: From Basics to Practice

2019-01-06 上传
Deep Learning, Vol. 1: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (909 of 1750) Language: English Format: 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 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.

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

2018-09-05 上传
首先，从文件的标题和描述中我们可以得知，文件名为“Sanet.st_Deep_Learning,_Vol._2_From_Basics_to_Practice_-_Andrew_Glassner_带书签.pdf”，这表明该文档是一本深度学习方面的书籍的第二卷，并且作者是Andrew ...

Deep Learning - From basics to practice - Vol. 1

2018-11-07 上传
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 ...

Deep Learning, Vol. 2 From Basics to Practice

2018-07-03 上传
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)...

deep learning from basics to practice vol.2

2023-06-21 上传
### 回答1： 《深度学习基础到实践 vol.2》是一本深度学习的相关书籍，适合有一定深度学习基础的读者进一步深入学习。本书是由神经网络与深度学习的专家所编写，分为第一部分和第二部分。第一部分主要讲述了全卷积...

深度学习基础与实践：卷1

点击了解资源详情
1 From Basics to Practice》是Andrew Glassner撰写的一本书，旨在从基础到实践全面介绍深度学习。书中涵盖了机器学习的基本概念、统计学、概率论、贝叶斯定理等核心知识，并深入探讨了神经网络、激活函数、反向...

深度学习基础到实践：卷1

点击了解资源详情
1 From Basics to Practice》是由Andrew Glassner编写的深度学习基础到实践的教程，涵盖了从机器学习基础知识到深度学习核心算法的多个章节。" 该书详细介绍了深度学习的基础知识，从机器学习的入门概念开始，逐渐...

《Deep Learning》Ian Goodfellow, Yoshua Bengio and Aaron Courville【带书签】

2016-08-04 上传
Bengio大神的《Deep Learning》全书已完稿，这里对目录整理了一下书签，方便查看
寒沧
  • 粉丝: 270
  • 资源: 161
上传资源 快速赚钱

最新资源