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Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

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Profile Icon Sebastian Raschka Profile Icon Yuxi (Hayden) Liu Profile Icon Vahid Mirjalili
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (85 Ratings)
Paperback Feb 2022 774 pages 1st Edition
eBook
Mex$902.99
Paperback
Mex$1128.99
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Free Trial
Arrow left icon
Profile Icon Sebastian Raschka Profile Icon Yuxi (Hayden) Liu Profile Icon Vahid Mirjalili
Arrow right icon
Free Trial
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4 (85 Ratings)
Paperback Feb 2022 774 pages 1st Edition
eBook
Mex$902.99
Paperback
Mex$1128.99
Subscription
Free Trial
eBook
Mex$902.99
Paperback
Mex$1128.99
Subscription
Free Trial

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Key benefits

  • Learn applied machine learning with a solid foundation in theory
  • Clear, intuitive explanations take you deep into the theory and practice of Python machine learning
  • Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices

Description

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.

Who is this book for?

If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

What you will learn

  • Explore frameworks, models, and techniques for machines to learn from data
  • Use scikit-learn for machine learning and PyTorch for deep learning
  • Train machine learning classifiers on images, text, and more
  • Build and train neural networks, transformers, and boosting algorithms
  • Discover best practices for evaluating and tuning models
  • Predict continuous target outcomes using regression analysis
  • Dig deeper into textual and social media data using sentiment analysis

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801819312
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Product Details

Publication date : Feb 25, 2022
Length: 774 pages
Edition : 1st
Language : English
ISBN-13 : 9781801819312
Category :
Languages :
Concepts :
Tools :

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Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.4
(85 Ratings)
5 star 77.6%
4 star 5.9%
3 star 2.4%
2 star 4.7%
1 star 9.4%
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Jason Mazzaroth Oct 17, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is critical for my PhD research and was necessary preparation for my prelim exam. This is an in-depth treatment of ML and it provides many reference publications inline, which are valuable for further research when you're about to publish papers related to the specific topics.
Amazon Verified review Amazon
acc_annon Mar 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Review of the drm-free pdf version available directly from the publisher (Packt): One of the best practical books on the subject! Covering wide range of topics with concrete non-trivial practical examples, python code, data sets, and with enough-but-not-too-much theory and references to provide further insight and understanding. More than 750 pages, plus book's massive source code is available on github!Although this is not a textbook, I'd recommend it to students too, as it helps dive into DS/ML topics from another, more pragmatic angle, thus great addition to one's studying and learning.Highly recommended.
Amazon Verified review Amazon
Fabiano R. Mar 03, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book discusses both "classical" and very recent machine learning models, algorithms, related frameworks like Scikit Learn and Pytorch, and how those can be leveraged to put the theory into practice.Speaking on theory, the authors usually present enough that the average user can have a reasonable understanding of underlying concepts before delving into code. I felt like beginners would benefit from some previous knowledge of some topics like vector and matrix operations, features and loss, even though they are present in the text.Still, when the reader is faced with topics that might be beyond its scope (such as generating new training data to address class imbalance, for example), the book does an excellent job of pointing the reader in the right direction, be it a published paper, a research article, or the URL for a library’s reference.I've read comments from people with theoretical knowledge, on the difficulty to translate that knowledge into practice and get a project started, and I think this book would be perfect in that scenario, as there are very clear explanations and code on how to process and treat datasets, train, evaluate and fine-tune models, use existing tools and libraries, and even readers who have practical experience might learn a thing or two... I sure did.Another use-case where I think this book would be invaluable is for people preparing for technical interviews, since it's very well organized and the theory is presented in such a concise manner, supported by actual working code.Earlier I mentioned that the authors don't usually go deep in theory, but when discussing Transformers, this was thankfully an exception... Its complex architecture merits more depth, and in my opinion they did a great job explaining its theory and how the different "components" interact, still had to look outside the book to understand some concepts but, again, the code was incredibly helpful and well written.I'm planning to start a new project in a few weeks and I am looking forward to using this book as a reference and applying the lessons I learned by reading it.
Amazon Verified review Amazon
Daniel Sinclair Mar 20, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As a physics student coming towards the end of my degree, I wish I had got my hands on this book before being confronted by neural nets and machine learning as a component of my modern computational techniques module.Machine Learning with PyTorch and Scikit-Learn is a great book that can take give someone, with basic matrix algebra and basic python skills, the knowledge to get a solid handle on ML and DL and the enthusiasm to pursue ML and DL further.It provides very visual, clear and attention grabbing explanations from neural net basics, all the way to advanced, current topics that are used to solve real-world problems in the current day. There are not massive blocks of boring text, everything is nicely organised and broken down into managagle steps.All code examples and mathematical equations are clear, well colour coded and concise - each diagram would make an excellent addition to a standalone tutorial page on its relevant subject.It truly allows you to carry out examples of all the essential topics of ML and DL, which as a near-beginner in the ML/DL space, was a realy confidence booster.PyTorch has been gaining some serious traction in the academic and research community, so getting yoiur hands on this book to have a good understanding of the PyTorch essentials seems like a sensible thing to do!
Amazon Verified review Amazon
Ross Cheung Feb 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Note: I received an advanced copy from the publisher, but I have read Sebastian Raschka's previous machine learning book and am familiar with his work. Off the top of my head the most comparable previous work is Aurélien Géron's "Hands-On Machine Learning with Scikit-Learn and Tensorflow". Both books have a similar format; the first half is a general introduction to machine learning principles and "non-Deep Learning" machine learning models, while the second half is dedicated to Deep Learning with a modern framework, though Raschka's book packs out at a whopping 800 pages.For the past few years Geron's book is a frequently recommended "first book" for people who are technically proficient but new to machine learning, for its all encompassing nature. How do these two books compare? First, Raschka's professor background shows; while Geron's book is a more friendly introduction, Raschka's book makes sure to explain all of the fundamentals. As an academic myself I really like this, and urge anyone new to ML working through this book to really understand the fundamentals when coding; it will help with ML engineering and research, and with helping to stay afloat in a fast moving field.Second, ML is a fast changing field, and a lot has happened since Geron's book was published in 2017. Although the 2019 2nd edition is a timely update, we've been seeing PyTorch eclipse Tensorflow in popularity especially in ML research. We've also seen the PyTorch ecosystem grow and become more involved, and Raschka's book makes sure to talk about the various PyTorch features.In addition, the field of Deep Learning has changed a lot. In the past four years image classification has ceased to be a hard topic for DL, transformers have massively changed the landscape, and fields like GANs, Graph neural networks, and Reinforcement learning has ceased to be esoteric academic topics and are all seeing applications in industry, and a strength of Raschka's book is addressing all of these.Finally, some advice for people who are reading through this book and getting up to speed with Deep Learning: read and work through the code examples. Many books about Machine Learning skimp over the programming aspects and/or guide the reader to call libraries, and as a result users are frustrated when it comes time to implement and use models. Don't neglect the coding aspects of getting ML models up and running. A strength of Raschka's book are the code examples, which walk through how to implement the models. His codes for PyTorch resemble what is used in production, and are generally high quality.Finally, if you've made it through this book, congrats! Go and use what you've learned for some fun projects, and also pair it with a good book or resource on deploying and monitoring your ML models.
Amazon Verified review Amazon
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