Hands On Machine Learning strikes *a perfect blend between application and theory Beginners to machine learning will find it clear *perfect blend between application and theory Beginners to machine learning will find it clear 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 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 not get ** too caught up in the details and he provides warnings when the next **caught up in the details and he provides warnings when the next 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 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 eep 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 still good 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 getting 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 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 En. 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 now 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 Semble Learning and Random Forests8 Dimensionality ReductionNEURAL NETWORKS AND DEEP LEARNING9 Up and Running with TensorFlow10 Introduction to Artificial Neural Networks11 Training Deep Neural Nets12 Distributing TensorFlow Across Devices and Servers13 Convolutional Neural Networks14 Recurrent Neural Networks15 Autoencoders16 Reinforcement Learning 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 grammar errors Or Any Real *errors any real *errors all Truly solid writingThe breadth of information covered if uite wide *at all Truly solid writingThe breadth of information covered if uite wide choice to start with Scikit Learn was interesting but makes sense on some level while he s introducing 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 glean 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 get you started The example code is uite good 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 good 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 grasp 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 on which types of layers activations regularization etc are most effective and gives good starting points for decent convergence His explanation of multi GPU Tensorflow was also uite good The Tensorboard section was also very usefulIn short if you want ONE book to get you into machine learning and Tensforlow is on your radar you can t go wrong with this one Highly recommended This is one of the best books you can get for someone who is just starting out in ML in its libraries such as Tensorflow It covers the basics very good As a book it is 55Once you are done with this book the ideal next step is the Deep Learning Book By Ian GoodfellowSadly my copy didn t look so good 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 Amazing book I would just like to point out that the description for the indle edition carries the disclaimer in bold that Graphics in this book are. Urlien Gron helps you gain 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 get startedExplore the machine learning landscape particularly neural netsUse scikit learn to track an example machine learni. Printed in black and white This is not true they are very much in colour and this makes a huge positive difference especially for graphical information presented in multiple dimensionsAs an enthusiastic hobbyist some of the descriptions of what is under the hood were slightly beyond my ability to fully comprehend However the book is so well

"WRITTEN THAT THIS BECOMES INSPIRING RATHER THAN FRUSTRATING SO "that this becomes inspiring rather than frustrating So next project is to improve my math 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 got 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 go 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 going on under the hoodI was so impressed with the deep learning portion of this *s going on under the hoodI was so impressed with the deep learning portion of this that I went back and read the rest of it I can t recommend this work highly enough 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 great reference nice detailed explanation with good references for further reading to deepen your nowledge 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 Examplecode presented in the book is not compatible with latest release of the tensorflow Reader will have to make the program work after lot of debugging and searching on net hence can be sometimes very frustrating

__Started With Few Chapters But Had To Leave It In__with few chapters but had to leave it in middle because of this issue But serves as a good 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. 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.

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk I've been involved in machine learning as a researcher / practitioner for 5 years, but used R for most of it and w

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk Read & Download Ó PDF, eBook or Kindle ePUB ↠ Aurxe9lien Gxe9ron 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

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk Hands On Machine Learning strikes a perfect blend between application and theory. Beginners to machine learning will find it clear to

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk review Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 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 practica

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk Aurxe9lien Gxe9ron ↠ 6 Read Read & Download Ó PDF, eBook or Kindle ePUB ↠ Aurxe9lien Gxe9ron

This is one of the best books you can get for someone who is just starting out in ML, in its libraries such as Tensorflow, It covers the basics very good. As a book, it is 5/5

Once you are done with this book, the ideal next step is the Deep Learning Book By Ian Goodfellow.

Sadly my copy didn't

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk The table of contents is missing in the Kindle preview.

THE FUNDAMENTALS OF MACHINE LEARNING

1. The Machine Learning Landscape (comment: probably the most lucid ML explanation I've ever read)

2. End to End Machine Learning Project

3. Classification

4. Training Models

5. Support Vector Machines

6. Decision Trees

7. Ensemble Learning and Random Forests

8. Dimensionality Reduction<

review Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Read & Download Ó PDF, eBook or Kindle ePUB ↠ Aurxe9lien Gxe9ron Aurxe9lien Gxe9ron ↠ 6 Read Example/code presented in the book is not compatible with latest release of the tensorflow. Reader will have to make the program work after lot of debugging and searching on net, hence can be sometimes very frustrating. Started with few chapters, but had to leave it in the middle because of this issue. But serves as a good starting point i

Kostenlos PDF Hands–On Machine Learning with Scikit–Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems – alphabetpreschool.co.uk Aurxe9lien Gxe9ron ↠ 6 Read Read & Download Ó PDF, eBook or Kindle ePUB ↠ Aurxe9lien Gxe9ron 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, especially for graphical information presented in multiple dimensions.

As an enthusiastic hobbyist, some of the descriptions of what i