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“Deep Learning with PyTorch Lightning: Swiftly build high-performance Artificial Intelligence (AI) models using Python (English Edition)”，作者：[Kunal Sawarkar]

Deep Learning with PyTorch Lightning: Swiftly build high-performance Artificial Intelligence (AI) models using Python (English Edition) Kindle电子书

英语版本 Kunal Sawarkar (作者) 格式: Kindle电子书

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A Hands-On Guide to build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper
Key Features

Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains
Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures
Train and build new algorithms for massive data using distributed training

Book Description
PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.
You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.
By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.
What you will learn

Customize models that are built for different datasets, model architectures, and optimizers
Understand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be built
Use out-of-the-box model architectures and pre-trained models using transfer learning
Run and tune DL models in a multi-GPU environment using mixed-mode precisions
Explore techniques for model scoring on massive workloads
Discover troubleshooting techniques while debugging DL models

Who this book is for
This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.
Table of Contents

PyTorch Lightning Adventure
Getting Off the Ground with Your First Deep Learning Model
Transfer Learning Using Pre-Trained Models
Ready-to- Use Models from Bolts
Time Series Models
Deep Generative Models
Semi-Supervised Learning
Self-Supervised Learning
Deploying and Scoring Models
Scaling and Managing Training

页数 : 共364页	更先进的排版模式: 已启用	快速翻书: 已启用
语种：英语

由于文件较大，下载时间可能较长。

基本信息

ASIN ‏ : ‎ B0964DNRY9
出版社 ‏ : ‎ Packt Publishing; 第 1st 版 (2022年4月29日)
出版日期 ‏ : ‎ 2022年4月29日
语言 ‏ : ‎ 英语
文件大小 ‏ : ‎ 24157 KB
标准语音朗读 ‏ : ‎ 已启用
X-Ray ‏ : ‎ 未启用
生词提示功能 ‏ : ‎ 未启用
纸书页数 ‏ : ‎ 364页

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美国亚马逊： 8 条评论

HW

A systematic way to learn about PyTorch Lightning

2022年6月20日 - 已在美国亚马逊上发表

I’ve come across PyTorch Lightening in a few projects, but never got a chance to learn it in a systematic way. This book gives a great overview of the package, and shows how to use the package to build the most commonly used deep learning architectures such as CNN, LSTM and Transformers.

What I like the most: the book walks through complete example codes of models, and offers detailed explanations almost line-by-line. This is something you don’t get by reading the package docs.

Other stuff that I like:
The book is well-organized with a clear structure and “Important Notes” — boxes highlighting key points
All the example codes are on Github

What I wish for:
a little sprinkle of humor here and there.
Some examples that compare & contrast with PyTorch. Explore their respective boundaries.
Go beyond the straight-forward examples for each model type, and discuss how the package can be used towards some common changes when working with different models or data types at a more advanced level

3 个人发现此评论有用

Daniel Armstrong

Wonderful content!!!

2022年6月20日 - 已在美国亚马逊上发表

I really enjoyed this book. I have wanted to learn more about “pytorch lightning” for a while since it is becoming one of the main wrappers around pytorch, and this book made this learning process enjoyable. I think the author did a great job creating content for deep learning practitioners with different levels of understanding. This can be exceptionally challenging, some authors get lost deep into fundamentals of deep learning, others expect an advanced vocabulary that hurts the learning process, but I found Kunal Sawarkar did a nice job keeping it interesting and packed full of information.

The book also covers a nice array of content with interesting data sets. I would recommend it to anyone that is looking to learn more about pytorch lighting. In particular I really like the speech recognition using flash, how he set up and talked about the traffic forecasting/time series problem. I also enjoyed reading and learning about GANs but I still feel that it is going to be a while before they become useful. Image captioning and contrastive learning are really interesting, and I will definitely try them out in “pytorch lighting”. It was also nice to see how easy it is to convert "Pytorch Lighting" models to ONNX format and server them with a flask app, I would have liked to see the same process for an NLP model.

1 个人发现此评论有用

Samuel

A nice intro to this new framework, Pytorch Lightning

2022年5月3日 - 已在美国亚马逊上发表

I mainly use Keras for work and have not used Pytorch frequently but this book definitely is a good starting point for someone would like to know more about Pytorch Lightning. Couple reasons why I like this book: (1) there are a lot of explanation for why the code is written and structure this way. So if you don’t have a very strong python background, you shall be able to get through this book without breaking a sweat. (2) it covers a wide range of applications, including CNN for image recognition, LSTM for time-series, as well as model deployment using Flask. A nice introduction to this relatively new framework.

2 个人发现此评论有用

Shantanu Solanki

Getting started with deep learning

2022年6月30日 - 已在美国亚马逊上发表

It's a nice book if you are getting started with deep learning. The first few chapters will help you take off in the field of deep learning and build your confidence in the PyTorch Lightning library. Then you will dive deep into the Deep Generative models, semi-supervised learning and self-supervised learning (all the cool stuff that the AI community is currently working on). I will definitely recommend it to both new DL learners as well as DL practitioners.

1 个人发现此评论有用

Sumanpreet Dosanjh

Great Read!

2022年6月30日 - 已在美国亚马逊上发表

As someone new to PyTorch Lightning, I found this book straightforward to understand. There are many concepts that I still am not reasonably confident in, such as time series and the various model types but reading this was helpful in that it explained things in great detail. The book's content is unique in that I did not find other books to touch on the topics and discussions in this much depth. I would recommend it to anyone in this field or even starting out.

1 个人发现此评论有用

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