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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. Programming experience is all you need to get started.

Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning

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Machine Learning, AI & more

Machine Learning

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Deep Learning

Language Processing (NLP, LLM)

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O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

Publisher ‏ : ‎ O’Reilly Media
Publication date ‏ : ‎ November 8, 2022
Edition ‏ : ‎ 3rd
Language ‏ : ‎ English
Print length ‏ : ‎ 861 pages
ISBN-10 ‏ : ‎ 1098125975
ISBN-13 ‏ : ‎ 978-1098125974
Item Weight ‏ : ‎ 3 pounds
Dimensions ‏ : ‎ 7.25 x 2 x 9.5 inches
Best Sellers Rank: #27,629 in Books (See Top 100 in Books) #5 in Computer Neural Networks #11 in Python Programming #54 in Artificial Intelligence & Semantics
Customer Reviews: 4.7 4.7 out of 5 stars (802) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });

13 reviews for Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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  1. Amazon Customer

    Great book to learn practical Machine Learning
    I have just finished Hands-On ML book and I cannot recommend it enough.I have been working as a Mobile Software Developer for 12 years and now I am thinking about trying something new. I remember some Math and Statistics from school but definitely not enough to get deep into the subject.From my experience, you can read the book and finish all the exercises without understanding any of the Math (although as author points out, it is beneficial if you understand the Math behind it – e.g. to understand why it works, read and implement papers).Book goes into the detail and explains the history of how we got there so it was fairly easy for me to follow and understand majority of the book. I missed this kind of detail from ML courses that I tried. You will also see significant papers explained – something that would be difficult for me to do alone at this point.However, one thing I appreciated the most were the exercises. In ML courses I tried, the exercises were simple and too easy to give you anything. Here it was a real challenge and I have a good feeling about what I learned by doing those exercises.There are also a lot of references for books or papers in case you want to focus on a specific area.One blind spot I am seeing though is focus on Keras/TensorFlow and GCP pipeline whereas the most examples on internet seem to be from PyTorch and AWS as a most popular cloud solution. However, as author points out, if you know one it will be easy for you to switch (I also reimplemented some of the PyTorch projects as part of exercises without too much difficulty). Still, I need to think about it and get some more PyTorch and AWS experience.

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  2. Shannon

    Excellent book for beginners!
    Excellent book for beginners! Easy to follow with complete practice data and code

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  3. David Alejandro Rodríguez Fernández

    Good book
    It’s a very good introductory book to supervised and unsupervised learning algorithms. It has a lot of code and brief explanations of the theory. It’s a very good start if you want to venture into the world of machine learning.

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  4. AmazonC

    Great content but need more work on the physical book quality
    The content in this book is fantastic. I like the writing style, making it more enjoyable to read. The color visuals and codes are helpful and practical. Definitely something I will want to use as reference when studying and working. The book is thick, about 2inches (I thought it would be half of that), so making it a bit inconvenience to carry around to school or library. But I think it can’t be thinner without no reducing the content inside. The paper quality could be better. It bleeds easily when using highlighters. The paper is quite thin that I could see text of the next page through the page I’m on. The glue type used to bind the book could be thicker, so it can hold these over 800 pages long book together longer. The glue is thinner compared to another book I have with similar number of pages, size and thickness.Overall, great content but need more quality check in terms of the physical appearance of the book.

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  5. zbuyer

    Not for the beginner
    Excellent information

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  6. Atul Deshmukh

    Nice
    Delivery and book perfect

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  7. Diego

    Hands on experience machine learning review
    Excellence content and print quality, strong fundamentals to understand machine learning concepts with practice

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  8. VINCENT NSENGIMANA

    Only for Experts |Econometricians can transform the whole world through reading this amazing book
    This book is the best one I have been looking for in my career development. I appreciate your quick service delivery, and I thank the Author for the tough work they have done with us.However, the cost of transport is so challenging (from the AMAZON/USA to Africa/Ghana is so expensive), and the book cover page would be a hard one instead of a paper cover, as the book is heavier.Thank you.

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  9. Jean

    Bom material e conteúdo muito enriquecedor

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  10. Klas

     

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  11. Joshua

    plenty of detail on all topics. Quite heavy and bulky so try not to have to take it places too much to avoid damaging it or your back lol

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  12. Markigno

    Ottimo libro per chi vuole padroneggiare il machine learning e il deep learning in modo pratico. Gli esempi con scikit-learn aiutano a capire bene i concetti di base, mentre le parti su Keras e TensorFlow mostrano come costruire e addestrare reti neurali anche complesse. L’autore riesce a spiegare con chiarezza temi tecnici come regularization, ottimizzazione e reti convoluzionali, senza mai risultare pesante. Una guida aggiornata, solida e ricca di codice, perfetta per chi lavora già con Python e vuole applicare davvero le tecniche di AI nei propri progetti.

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  13. John Gale

    This is a big 835 page tome – I have yet to deeply read it all! A quality print from O’Reilly in its third edition (2023), its 19 sections takes us in good detail through the modern AI landscape with clear explanations using just the essential mathematical support. I particularly like the excellent illustrations/diagrams and numerous coding examples in Python and its supportive environment. Included is a decently detailed index and exercises for the brave reader. This book will remain a useful reference for the foreseeable future. Great value.

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    Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
    Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

    Original price was: $89.99.Current price is: $49.50.

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