Download PDF Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD By Jeremy Howard,Sylvain Gugger
Download PDF Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD By Jeremy Howard,Sylvain Gugger
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Ebook About Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your workGain insight from the foreword by PyTorch cofounder, Soumith ChintalaBook Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Review :
What an amazing book! What an amazing venture Sylvain and Jeremy have undertaken!I've done parts of the fast.ai video course in the past. I was very excited that a book version was coming, and in this kindle edition they don't disappoint. For those who prefer written materials to videos, this will be an exciting release.I haven't finished all the materials in the book, but I've read a good way and while it's a different experience to doing the course online, I have been enjoying it so far. The book is well written, well thought-out and the ideas explored are interesting in and of themselves.For those who use kindle devices, I'm happy to report that the book opens on an old Kindle 2, as well as on iPad, iPhone and web versions of the Kindle reading application. Screenshots above are taken from the web version. You can see in one of them that the formatting is really well handled -- you can make highlights in the code samples. (Those of you who read technical books on their kindles will know that it is RARE that the publisher makes the effort to handle the formatting of these books properly -- quite often they just make images of the code snippets in the book, making for a bloated file size of the book and unusuable content from the perspective of the reader. Luckily this book is REALLY WELL FORMATTED. Thank you, O'Reilly (and Sylvain and Jeremy presumably as well, for their open-access formatting of the book which is on Github too)).I'll let others more knowledgeable than me comment on the content of the book, but for this early-stage deep learning student, this book is inspiring, clearly written and a great asset in my studies going forward. If the point of reading an introductory book on how to use machine learning or deep learning is to learn the concepts, then apply them and learn the language enough to be able to code new programs or do new exciting things with A.I., then this book fails.First, it oversells. It states that you don't need a degree in math, you don't need to be a programmer or a data scientist. Well, it might just mean you have to take some extra calculus or python classes before reading this book. And that is ok. That's not the worst part. And it would not be that bad if you could just copy the code and learn from it. If you could just type in word-for-word, symbol-by-symbol the code in the book but.... You will soon be met with frustration because the code that is written in this book is outdated and does not work. Two examples from early in the book are on page 161 about Stochastic Gradient Descent. The following code: params.data -= lr * params.grad.data will give an error code. Another code on page 70 about "DataBlocks" uses another bit of language, "splitter=Random.Splitter(valid_pct=0.3,seed=42). This also does not work. I gave up trying to learn this crap at this point. Its hard enough learning Python, regex and the author's use of ".this" and "underscore_that" to not have it work in the end. No wonder you can download the book off of GitHub. Its questionable if its worth the paper its printed on. If Jeremy Howard can fix his code to where it will work on a consistent basis without having to re-learn more stuff that will eventually become outdated as well, then he might have something here. If not, oh well. Someone else will come along and do just that. Read Online Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Download Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD PDF Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Mobi Free Reading Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Download Free Pdf Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD PDF Online Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Mobi Online Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Reading Online Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD Read Online Jeremy Howard,Sylvain Gugger Download Jeremy Howard,Sylvain Gugger Jeremy Howard,Sylvain Gugger PDF Jeremy Howard,Sylvain Gugger Mobi Free Reading Jeremy Howard,Sylvain Gugger Download Free Pdf Jeremy Howard,Sylvain Gugger PDF Online Jeremy Howard,Sylvain Gugger Mobi Online Jeremy Howard,Sylvain Gugger Reading Online Jeremy Howard,Sylvain GuggerBest Dirty Wedding By Crystal Kaswell
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