- fastai与PyTorch深度学习实践指南(影印版)(英文版)
- - 作者：(美)杰瑞米·霍华德//(法)西尔万·古戈尔|责编:张烨
  - 出版社：东南大学
  - ISBN：9787564194543
  - 出版日期：2021/04/01
  - 页数：594
- 售价：67.6

内容大纲
    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，这是首个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最新深度学习技术；
    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。
作者介绍
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

内容大纲

作者介绍

目录

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