作者：(美)Jeremy Howard，(法)
页数：22,594页
出版社：东南大学出版社
出版日期：2021
ISBN：9787564194543
电子书格式：pdf/epub/txt
内容简介
深度学习往往被视为数学博士和大型科技公司的专属领域。但正如这本实践指南所展示的那样，熟练使用Python的程序员只需很少的数学背景、少量的数据和最少的代码，就可以在深度学习方面取得令人印象深刻的成果。怎么样才能做到？使用fastai，这是第一为最常用的深度学习应用提供一致接口的库。本书作者Jeremy Howard和Sylvain Gugger是fastai的创建者，他们向你展示了如何使用fastai和PyTorch在各种任务上训练一个模型。你还将逐步深入了解深度学习理论，以便充分理解幕后的算法。在计算机视觉、自然语言处理、表格型数据和协同过滤中训练模型；学习在实践中至关重要的第一深度学习技术；通过了解深度学习模型的工作原理，提高准确性、速度和可靠性；了解如何将你的模型转化为Web应用；从头开始实现深度学习算法；考虑你的工作所带来的道德影响；从PyTorch联合创始人Soumith Chintala的前言中获得启示。
目录
Preface
Foreword
Part I. Deep Learning in Practice
1. Your Deep Learning Journey
Deep Learning Is for Everyone
Neural Networks: A Brief History
Who We Are
How to Learn Deep Learning
Your Projects and Your Mindset
The Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)
Your First Model
Getting a GPU Deep Learning Server
Running Your First Notebook
What Is Machine Learning?
What Is a Neural Network?
A Bit of Deep Learning Jargon
Limitations Inherent to Machine Learning
How Our Image Recognizer Works
What Our Image Recognizer Learned
Image Recognizers Can Tackle Non-Image Tasks
Jargon Recap
Deep Learning Is Not Just for Image Classification
Validation Sets and Test Sets
Use Judgment in Defining Test Sets
A Choose Your Own Adventure Moment
Questionnaire
Further Research
2. From Model to Production
The Practice of Deep Learning
Starting Your Project
The State of Deep Learning
The Drivetrain Approach
Gathering Data
From Data to DataLoaders
Data Augmentation
Training Your Model, and Using It to Clean Your Data
Turning Your Model into an Online Application
Using the Model for Inference
Creating a Notebook App from the Model
Turning Your Notebook into a Real App
Deploying Your App
How to Avoid Disaster
Unforeseen Consequences and Feedback Loops
Get Writing!
Questionnaire
Further Research
3. Data Ethics
Key Examples for Data Ethics
Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits
Feedback Loops: YouTube’s Recommendation System
Bias: Professor Latanya Sweeney “Arrested”
Why Does This Matter?
Integrating Machine Learning with Product Design
Topics in Data Ethics
Recourse and Accountability
Feedback Loops
Bias
Disinformation
Identifying and Addressing Ethical Issues
Analyze a Project You Are Working On
Processes to Implement
The Power of Diversity
……
Part II. Understanding fastai’s applications
Part III. Foundations of Deep Learning
Part IV. Deep learning from Scratch
Index
Foreword
Part I. Deep Learning in Practice
1. Your Deep Learning Journey
Deep Learning Is for Everyone
Neural Networks: A Brief History
Who We Are
How to Learn Deep Learning
Your Projects and Your Mindset
The Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)
Your First Model
Getting a GPU Deep Learning Server
Running Your First Notebook
What Is Machine Learning?
What Is a Neural Network?
A Bit of Deep Learning Jargon
Limitations Inherent to Machine Learning
How Our Image Recognizer Works
What Our Image Recognizer Learned
Image Recognizers Can Tackle Non-Image Tasks
Jargon Recap
Deep Learning Is Not Just for Image Classification
Validation Sets and Test Sets
Use Judgment in Defining Test Sets
A Choose Your Own Adventure Moment
Questionnaire
Further Research
2. From Model to Production
The Practice of Deep Learning
Starting Your Project
The State of Deep Learning
The Drivetrain Approach
Gathering Data
From Data to DataLoaders
Data Augmentation
Training Your Model, and Using It to Clean Your Data
Turning Your Model into an Online Application
Using the Model for Inference
Creating a Notebook App from the Model
Turning Your Notebook into a Real App
Deploying Your App
How to Avoid Disaster
Unforeseen Consequences and Feedback Loops
Get Writing!
Questionnaire
Further Research
3. Data Ethics
Key Examples for Data Ethics
Bugs and Recourse: Buggy Algorithm Used for Healthcare Benefits
Feedback Loops: YouTube’s Recommendation System
Bias: Professor Latanya Sweeney “Arrested”
Why Does This Matter?
Integrating Machine Learning with Product Design
Topics in Data Ethics
Recourse and Accountability
Feedback Loops
Bias
Disinformation
Identifying and Addressing Ethical Issues
Analyze a Project You Are Working On
Processes to Implement
The Power of Diversity
……
Part II. Understanding fastai’s applications
Part III. Foundations of Deep Learning
Part IV. Deep learning from Scratch
Index
