# ( [Buch] Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ) BY Aurxe9lien Gxe9ron – stoptheworldcup.co.uk

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__Release Of The Tensorflow Reader Will Have To Make The__of the tensorflow Reader will have to make the work after lot of debugging and searching on net hence can be sometimes very #work after lot of debugging and searching on net hence can be sometimes very Started with few chapters but had to leave it in the middle because of this issue But serves as a ood starting point in terms of theoretical aspects on neural networks cnn rnnAt the same time I was unable to find a book dedicated on deep learning with tensorflow Not a bad book at all but incompatible with latest version of tensorflow Can be used as a reference for learning understanding cnns rnn etc 5 for the first half of the book scikit learn 3 for the second half Tensor Flow Nice examples with Jupyter notebooks Good mix of practical with theoretical The scikit learn section is a The Last True Explorer great reference nice detailed explanation withood references for further reading to deepen your knowledge The tensor flow part is weaker as examples become complex Chollet s book Deep Learning with Python which uses Keras is much stronger as the examples are easier to understand as Keras is a simple layer over tensor flow to ease the use Also Chollet explains the concepts better and nicely annotates his codeBuy this book for scikit learn and overall best practise for machine learning and data scienceBuy Chollet s Deep Learning using Python for practical deep learning itselfOverall still a practical book with Jupyter Notebook supplementary material I ve been involved in machine learning as a researcher practitioner for 5 years but used R for most of it and was originally reluctant to move to Python learning pandas numpy scipy and scikit learn is an intimidating hill to climb when you re already so comfortable in RI ot this book for the deep learning portion about half of the overall book length and was shocked at the clarity of the conceptual explanations and code implementations I ve read many extensive explanations of important neural network architectures FFNs CNNs RNNs and none of them were this clear and intuitive Within 5 days I was able to o from having zero deep learning experience to easily implementing complicated architectures with TensorFlowMany people recommend Keras as an alternative to TensorFlow and I agree but reading this book allowed me to understand the structure of the underlying code enough to use Keras much effectively than if I had just started there and never learned what s Location, Location, Damnation (The Brackenford Cycle going on under the hoodI was so impressed with the deep learning portion of this book that I went back and read the rest of it I can t recommend this work highly enough Amazing book I would just like to point out that the description for the kindle edition carries the disclaimer in bold that Graphics in this book are printed in black and white This is not true they are very much in colour and this makes a huge positive difference This is one of the best books you canet for someone who is just st. Graphics in this book are printed in black and whiteThrough 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 howBy using concrete examples minimal theory and two production ready Python frameworksscikit learn and TensorFlowauthor Networks11 Training Deep Neural Nets12 #Distributing TensorFlow Across Devices And #TensorFlow Across

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Convolutional Neural Recurrent Neural Networks15 Autoencoders16 ReinforcementNeural Recurrent Neural Networks15 Autoencoders16 Reinforcement Hands On Machine Learning strikes a perfect blend between application and theory Beginners to machine learning will find it clear to follow and will be able to build complete systems within a few chapters while those with an intermediate level of experience will find a comprehensive up to date Thrones, Dominations guide to this exciting fieldPros Practical The book focuses on examples and implementations of the algorithms rather than the mathematics allowing readers to uickly build their own machine learning models Readable Geron does notet too caught up in the details and he provides warnings when the next section is heavy on theory Online Jupyter Notebooks The Jupyter Notebooks that accompany this book and can even be viewed for free with no purchase from the author s GitHub are worth the entire purchase price They feature examples of all the code in the book plus additional explanatory material The end of chapter solutions to the coding exercises are I Hela Cnau gradually being added to the notebooks Up to date The leading edge of machine learning and in particular deep learning is constantly shifting and Geron does his best to keep the notebooks updated Multiple times I have read an ML paper and then found the techniue implemented in the notebooks within weeks of the publication of the article Some of the techniues in the book may not be at the absolute forefront of the field but they are stillood enough for learning the fundamentals Engaging The book is a joy to read and the author is uick to respond to issues pointed out by readers in the book or in the Jupyter Notebooks Clearly the author enjoys machine learning and teaching it to othersCons Experts may find this book lacks enough depth because it is focused on etting up and running rather than optimization It also is specifically aimed towards Python and Tensorflow for deep learning so those looking for implementations in other frameworks will have to search elsewhere Due to the rapidly evolving nature of the field a print book on machine learning will always need to be periodically re issued to stay on top of all the developments Nonetheless the fundamentals covered in this book will remain relevant and the Jupyter Notebooks are constantly updated with new techniuesFinal Line If you have some basic experience with Python loops conditionals dictionaries and especially Numpy and zero to a medium level of experience with machine learning this book is an optimal choice I would recommend it both for those wishing to self study and uickly develop working models and for students in machine learning who want to learn the implementations of theoretical coursework I have enjoyed spending time working through the chapters and the exercises and have found this book extremely useful. Ng project end to endExplore several training models including support vector machines decision trees random forests and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures including convolutional nets recurrent nets and deep reinforcement learningLearn techniues for training and scaling deep neural netsApply practical code examples without acuiring excessive machine learning theory or algorithm detai. .

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.Arting out in ML in its libraries such as Tensorflow It covers the

**#basics very ood As a book it is 55Once you are done with #**very Jace's Pet good As a book it is 55Once you are done with book the ideal next step is the Deep Learning Book By Ian GoodfellowSadly my copy didn t look soood If it were an under 300 book I would have let it slide but when the book costs 1450 Which it is totally worth it I expected a much better copy This has to be at the top of my list of most highly recommended books The amount of material it covers is awesome and I can find almost no fault with it The writing is extremely clear easy to read written in impeccable English Very well edited I don t think I came across any spelling or rammar errors or any real errors at all Truly solid writingThe breadth of information covered if uite wide The choice to start with Scikit #Learn was interesting but makes sense on some level while he s #was interesting but makes sense on some level while he s the basic machine learning concepts Simple machine learning techniues like logistic regression data conditioning dealing with training validation test set Even if you ve read about these concepts a million times you might still lean useful information from these pagesThe Tensorflow section is also super well done Straightforward setup instructions pretty intelligible explanation of the basic concepts variables placeholders layers etc to Piraten! get you started The example code is uiteood and the notebooks are uite complete and seem to work well with maybe a few tweaks and additional setup for some I also found that the notebooks show examples than what s in the book which can be niceI only went really hands on with the reinforcement learning notebook and found that it was well done and a ood base to start my own work from Even just having a section on reinforcement learning is very rare in a book of this style and Geron s samples and explanations are really solid He obviously has a strong rasp of many varied fields within deep learning and that includes reinforcement learning The only thing I wish it had was an A3C sample to make my life

*that much easier But you can t have everythingI really liked his tips*much easier But you can t have everythingI really liked his tips which types of layers activations regularization etc are most effective and The Complete Guide To Surfing Your Best givesood starting points for decent convergence His explanation of multi GPU Tensorflow was also uite From Tree Dwellings To New Towns good The Tensorboard section was also very usefulIn short if you want ONE book toet you into machine learning and Tensforlow is on your radar you can t o wrong with this one Highly recommended The table of contents is missing in the Kindle previewTHE FUNDAMENTALS OF MACHINE LEARNING1 The Machine Learning Landscape comment probably the most lucid ML explanation I ve ever read2 End to End Machine Learning Project3 Classification4 Training Models5 Support Vector Machines6 Decision Trees7 Ensemble Learning and Random Forests8 Dimensionality ReductionNEURAL NETWORKS AND DEEP LEARNING9 Up and Running with TensorFlow10 Introduction to Artificial Neural. Urlien Gron helps you ain an intuitive understanding of the concepts and tools for building intelligent systems Youll learn a range of techniues starting with simple linear regression and progressing to deep neural networks With exercises in each chapter to help you apply what youve learned all you need is programming experience to A Streetcar Named Desire. By Nicola Onyett (Philip Allan Literature Guide get startedExplore the machine learning landscape particularly neural netsUse scikit learn to track an example machine learni.

Aurxe9lien Gxe9ron