Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
Product ID: 169894079
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“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production Review: Manning rules. - While the content of the books published by Manning varies, the vast majority of their books are excellent, and I value their policies. I typically buy the eBook + print, starting to read and learn immediately while the paperback arrives for my collection "whenever". Better yet, Manning's books are decently priced and the publisher also provides early access to books as they are being written. As the eBook progresses and you read, you can be certain that a printed copy/final eBook arrives "when done". This is extremely important in the fast-paced topics (e.g., machine learning) that these books address, and I have recommended several books of their catalogue to students in the past. This book, as well as many others of their catalogue, are pretty much hands-on and come with complementary code examples (Manning Live Book). This provides a way to get going quickly and reproduce the examples from the book without hassle. Review: Great start for deep learning - This book starts off slow, but goes into detail about PyTorch, tensors, back propagation, etc. It is a great introduction to the field and helps to understand convolutions, resnets, etc. One large basic component that it is currently lacking is a chapter on language models and attention. Hopefully the second edition will include this information down the line. Finally, the networks here are mostly sequential. The final example that takes part in the last half of the book is not incredibly useful in my opinion, but it does help to see a DL project all the way through. A few chapters about branching networks, combining 1D/2D/3D information, cross attention, and some of the current interesting complexity in the field would be welcome.
| Best Sellers Rank | #563,925 in Books ( See Top 100 in Books ) #192 in Computer Neural Networks #205 in Data Processing #404 in Python Programming |
| Customer Reviews | 4.5 out of 5 stars 154 Reviews |
J**R
Manning rules.
While the content of the books published by Manning varies, the vast majority of their books are excellent, and I value their policies. I typically buy the eBook + print, starting to read and learn immediately while the paperback arrives for my collection "whenever". Better yet, Manning's books are decently priced and the publisher also provides early access to books as they are being written. As the eBook progresses and you read, you can be certain that a printed copy/final eBook arrives "when done". This is extremely important in the fast-paced topics (e.g., machine learning) that these books address, and I have recommended several books of their catalogue to students in the past. This book, as well as many others of their catalogue, are pretty much hands-on and come with complementary code examples (Manning Live Book). This provides a way to get going quickly and reproduce the examples from the book without hassle.
D**D
Great start for deep learning
This book starts off slow, but goes into detail about PyTorch, tensors, back propagation, etc. It is a great introduction to the field and helps to understand convolutions, resnets, etc. One large basic component that it is currently lacking is a chapter on language models and attention. Hopefully the second edition will include this information down the line. Finally, the networks here are mostly sequential. The final example that takes part in the last half of the book is not incredibly useful in my opinion, but it does help to see a DL project all the way through. A few chapters about branching networks, combining 1D/2D/3D information, cross attention, and some of the current interesting complexity in the field would be welcome.
J**Z
Boost your understanding, you skills and save you tons of time!
I purchased this book quite a few days ago and I cannot stop reading it! Although I am somewhat experienced with both PyTorch and Deep Learning, I took a course in Deep Learning and read various articles online, I cannot emphasize more how much I like this book. It organizes both PyTorch and Deep Learning material in a nice and understandable way reaching a broad audience. It is not spoon fed but it is not too technical either. It is exactly what I needed it. I strongly recommend this book and guarantee its value, just buy it and read it as soon as possible.
S**N
overall good
good: * code example is working and helpful for understanding the concepts * include real problem solving techniques bad: * doesn't explain things straight forward.
R**K
Excellent Deep Learning Introduction to PyTorch
I found this book to be an excellent introduction to PyTorch. Not only is the introduction to PyTorch thorough, but its use in Deep Learning is highly documented and explained. The author doesn't scrimp on either introduction concepts or in supporting code. He spends over 475 pages to get it all spelled out carefully in text, pictures , and graphs that should satisfy the most severe critics. Python is a powerful general purpose language that has a performance bottleneck that PyTorch overcomes by accessing Nvidia GPUs to do the complex mathematical computations. Having, in effect, a Python program that can run 120 times faster than usual can make your program powerful enough to do some real research. You can design intelligent robots, self steering vehicles, house automation systems, and business research programs with this knowledge.
B**I
Subpar print and paper quality
I just got the book, so this is not a review of the content, rather by its "cover". The quality of the paper is really bad and the book is printed in black and white; together they make the illustrations hard to read. To be honest, I'm so disappointed by the print quality that I'm pondering returning the book and just reading the digital version. Such a bummer!
S**H
Of all the books out there on deep learning and python frameworks, this is the one to buy and read!
In one day, I am well into Chapter 5, but I can already state confidently that this is an excellent book! This seems to me the best single reference for learning PyTorch and deep learning in a hands-on inviting way. The write is clear and to the point. The github repo with jupyter notebooks follow perfectly with the text images and they all work so far. Chapter 5 is where the mathematics of deep learning is presented but it is does very clearly using Python code/formulas. It strikes a nice balance in theory and practice and is never boring. Of all the books out there on deep learning and the various Python frameworks, this is the one to buy and read! I can say that because I have over the past 5+ years bought them all. I rarely get passed first few chapters before the authors lose my interest but not this one. It is highly recommend and fun to read and use.,
S**D
Bridging Theory and Practice in ML
This book does an excellent job of bridging theoretical concepts in machine learning with practical implementation using PyTorch. The topics range from basic to advanced, covering a broad spectrum of ML and DL tasks. While the book is incredibly informative, beginners might find some sections challenging.
G**E
Testo molto chiaro scritto dai programmatori del framework pytorch.
M**C
Book written by deep experts, covering a lot of ground.
S**U
Book came in black and white : disappointing Book content: Really good introduction to deep learning intuition. Not particularly a fun of the last example of the book with CT scans (aka using 3d images) . An example with x-rays (2d images) scans will have been more appropriate for an introductory book, and easier to follow. Overall, very pleased
J**Y
Great book, But the print quality is bad. Not even worth for 300 rupees. pdf is available online. Go and get it printed. I got it printed for 250 rupees.
P**O
- Revisit all the basics in a very ludic way - Concrete implementation for Medical Computer Vision problem (~1/3 book) - Won't make you step up in the field as intermediate/advanced user (Kaggle and papers are still the best places for that)
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