fastai与pytorch深度学实践指南(影印版) 人工智能 (美)杰瑞米·霍华德,(法)西尔万·古戈尔
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￥ 87.85 5.2折 ￥ 169 全新
库存7件
作者(美)杰瑞米·霍华德,(法)西尔万·古戈尔
出版社东南大学出版社
ISBN9787564194543
出版时间2021-04
版次1
装帧平装
开本16
页数588页
字数759千字
定价169元
货号xhwx_1202331554
上书时间2024-11-16
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目录：
preface
foreword
part i. deep learning in practice
1. your deep learning journey
deep learning is for everyone
neural works: 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 doesnt matter)
your first model
getting a gpu deep learning server
running your first notebook
what is machine learning?
what is a neural work?
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 loo
get writing!
questionnaire
further research
3. data ethics
key examples for data ethics
bugs and recourse: buggy algorithm used for healthcare benefits
feedback loo: youtubes remendation system
bias: professor latanya sweeney "arrested"
why does this matter?
integrating machine learning with product design
topics in data ethics
recourse and accountability
feedback loo
bias
disinformation
identifying and addressing ethical issues
analyze a project you are working on
processes to implement
the power of diversity
……
part ii. understan fastais applications
part iii. foundations of deep learning
part iv. deep learning from scratch
index
内容简介：
本书作者jeremyhoward和ylvaingugger是fatai的创建者，他们向你展示了如何使用fatai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?学在实践中至关重要的新深度学技术?通过了解深度学模型的工作，提高准确、速度和可靠?了解如何将你的模型转化为web应用?从头开始实现深度学算法?虑你的工作所带来的道德影响?从pytorch联合创始人oumithchintala的前言中获得启示
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