Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems PDF AZW3 EPUB MOBI TXT Download

Through a series of recent 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 practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.Explore the machine learning landscape, particularly neural netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the Tensor Flow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets.
Aurélien Géron
O'Reilly Media; 2nd edition (October 15, 2019)
856 pages
File Size: 40 MB
Available File Formats: PDF AZW3 DOCX EPUB MOBI TXT or Kindle audiobook Audio CD(Several files can be converted to each other)
Language: English, Francais, Italiano, Espanol, Deutsch, chinese
Aurélien Géron is a machine learning consultant and trainer. A former Googler, he led YouTube’s video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst (a leading Wireless ISP in France) from 2002 to 2012, and a founder and CTO of two consulting firms — Polyconseil (telecom, media and strategy) and Kiwisoft (machine learning and data privacy). <div id="
  • This book gives you a hands-on approach to learning by doing. As opposed to the trendy deep learning books that dive deep into the weeds from the start, this book starts with the more traditional ML approaches (the Scikit-learn part) giving you a great deal of context and practical tools for solving all kinds of problems. Only after does he transition into deep learning concepts, giving you both a great overview and the background to understand when and where to apply the various techniques. Its code-focused so you’ll have the option to run working code on real problems throughout the book.Most important for me, he focuses on explanation over hand-wavy equations that are rampant in other ML books. I say hand-wavy because they typically go like so: “Here’s a hard concept. Rather than explain it well, I’ll give you some linear algebra and calculus equations, remind you that this is stuff you should have learned in high school, and then move on.” Authors probably feel justified in doing this, but after reading a book like this you understand what they are really doing: Skipping the hard-part of breaking difficult concepts down into chunks that can be consumed by a competent programmer, who is perhaps not an expert in “high school” math. Moreover, this author does so without dumbing down the content. That’s the mark of someone who well understands both the content and the audience.This book is long and dense, and serves as both a guide and a reference. It is not a quick read / overview or light reading type book.
  • I’ve read all of the predominant machine learning related python books and this one is by far the best one. I was excited to see the second edition of this book come out. It is packed with new information (1.5x the length of the first edition) and updated for TensorFlow 2. I have the Kindle edition and find it very helpful to highlight key points. I look forward to receiving the print edition as well once it is released.EDIT: Just received the print edition of the book and it’s in color! The first edition wasn’t. This is a pleasant surprise as it makes it easier to read with various charts and graphics.
  • While I enjoy learning from this book, the math font in kindle edition is a mess which makes the reading unpleasant. I know to some this probably shouldn’t be a deal breaker but for someone who wants to move from hard copy to kindle, it was a disappointment.
  • This is an update for my previous review.Recently, I gave one star for the poor ebook experience but with author’s comment I realized the publisher updated the ebook and now everything is great in the ebook.As the name suggests, the book gives you a really hands-on experience on machine learning. This covers most of the recent main advancements in the field.
  • I’m very pleased with this book. I enjoy the little bits of humor here and there, and it does a great job not glossing over important details that might be a stumbling block for someone. I’m quite comfortable with python however I appreciated that he did go into depth on setting up virtual environments and best practices. I remember years back when I was starting that whole concept tripped me up so much, having this explained so well is going to save someone a lot of time. Also his code seems so far to be written in a very thoughtful way and has them all on github. He also goes into lots of gotchas and tips and tricks that just overall seem to add a certain maturity to his writing. He has obviously very well versed in machine learning.Overall I would recommend. It’s been much more interesting than I expected.
  • Aurelien did it again!Whether you are a data scientist looking to start building predictive models in Python, or a software developer looking to become an ML engineer, look no further!The excellent balance between theory/background and implementation that was present in the first edition is kept, with the essential material additions made (e.g. the unsupervised learning in the “classical ML” part, or the Keras API, which is quickly becoming the most popular way to use TensorFlow).Needless to say, the Jupyter notes accompanying each chapter are more than helpful.Also, as a cherry on top, the illustrations in the printed version are now in color, which makes it even easier to read.In summary, this book is an absolute must-have for a Python-rooted data scientist / ML engineer!
  • I’m finishing up an MCS in Data Science from UIUC and I can tell you bar none that this book should be required reading in this subject. The required ML course at school was so confusing and they assumed WAY too much. Reading over the same topics in this book was like night and day in terms of explaining things in a way that makes sense. The images, graphs, and tables are clear and help a lot by providing visuals to the text explanation. I did notice a few typos but so far nothing critical. This is not a light read as it comes in at almost 800 pages but taking it model-by-model is easy to do.
  • The book was worth the wait! The publication quality of the print edition is great. Love the color illustrations. The one thing that I miss is that having bought the print edition, it would be sweet to have an offer to acquire the electronic edition at a reduced price but since Amazon now seems to be handling O’Reilly book sales and probably wants to sell as many Kindle editions as possible, a PDF copy of Hands-On Machine Learning, 2nd Ed., does not seem to be in my future at a bargain price. My review is preliminary – I’ve read bits of the online draft version-and the clarity and superb organization of Géron’s writing convinced me that I wanted a finished copy of the book. My current avocational interest is Reinforcement Learning and Géron gives an excellent overview – to dive deep, one would probably still want to refer to Sutton & Barto’s 2nd Ed. book (available on Amazon or for free online) or David Silver’s excellent 2015 UCL lectures, also available online.. I will slowly work my way through Géron’s book in its entirety but my primary reason for owning the book is as a reference. It makes a great roadmap to the current state of machine learning and, best of all, it makes learning about ML fun!
  • If you have the budget to only buy one ML book, I would suggest going for this one. It covers most of the field in one book. Get a datacamo subscription too and you can break into the DS career.
  • Have been advised by many people this is possibly the best book on ML but held off on owning a hard copy as I found it a bit expensive so I grabbed this one roughly 50% off. The level of detail is amazing and everything ML related is nicely explained. It’s nice to see the book was printed in colour which makes the code easier to follow and reproduce. I also liked the layout very much and found it helped to make the book flow – will happily read this cover to cover. The quality of the paper is on thin side but to be fair the content is worth more – I own other similar size ML books printed in black and white that cost more with half the content because it was printed on thick paper. Highly recommended for anyone with an interest in ML.
  • This book was just released, and I’m only a few chapters into it, but I can already attest that it’s an excellent approach to the topic. It reads like the book I would have written (or perhaps would have liked to have written) if I were to pen a volume on artificial intelligence. It’s a long-overdue addition to the subject, and I’m thrilled to see it executed so well.
  • This is an excellent book for machine learning, data science and deep learning. The print quality is great, the author’s style of explaining concepts and going into enough depth of the subject is also amazing. I use this as my reference for any machine learning project. It is not just for beginners, it also teaches a lot of advanced concept including creating your custom models, optimisers and loss functions in Tensorflow. It goes from really basic machine learning modelling like linear or logistic regression to advance Deep Learning all the way to generative modelling. It assumes basic prior knowledge in python.
  • Good book about the machine learning and deep learning. with pratical codes using sklearn, tf2.0. but it is not easy to form a complete solution just from this book. It is worthy read a few times on some chapters to understand better. wonder any alternatives or complementary books…
  • About Aaovo.com :
    We are committed to sharing all kinds of e-books, learning resources, collection and packaging, reading notes and impressions. The book resources of the whole station are collected and sorted by netizens and uploaded to cloud disk, high-definition text scanning version and full-text free version. This site does not provide the storage of the file itself.
    Description of file download format: (Note: this website is completely free)
    The e-books shared by this site are all full versions, most of which are manually refined, and there are basically no omissions. Generally, there may be multiple versions of files. Please download the corresponding format files as needed. If there is no version you need, it is recommended to use the file format converter to read after conversion. Scanned PDF, text PDF, ePub, Mobi, TXT, docx, Doc, azw3, zip, rar and other file formats can be opened and read normally by using common readers.
    Copyright Disclaimer :
    This website does not store any files on its server. We only index and link to the content provided by other websites. If there is any copyrighted content, please contact the content provider to delete it and send us an email. We will delete the relevant link or content immediately.
    Download link description :
    We usually use Dropbox, Microsoft onedrive and Google drive to store files. Of course, we may also store backup files in other cloud content management service platforms such as Amazon cloud drive, pcloud, mega, mediafire and box. They are also great. You can choose the download link on demand.

    File Size: 40 MB