Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition 2nd Edition, Kindle Edition

Jan 26, 2020 Review Notes13. Some programs are giving "cublas64-100.dll" file not found error. Is it possible for authors to zip this dll file and post it on this book's Github page please?Due to fast updating libraries/tools of Python, R Programming etc14. Source code of Packt Published books are not working.15. If Packt publishing & authors of Packt published books can regularly test the code filesand upload updated code file these books will be very useful for many years16a. Below given book is also giving errors similar to this book's errors.16b. Please help in fixing the source code files of the following book: "Deep Learning with R for Beginners: Design neural network models in R using TensorFlow, Keras, and MXNet by Mark Hodnett etc" have similar errorsJan 25, 2020 Review Notes:Downloaded latest update of the source code files from Github.Ran cifar10_predict.py program of page 131. It ran without errors and gave output results.However, the output gave [4 4]This output is saying both "standing cat imge" and "dog image" belong to same class of four.This result may be wrong, due to one or both of the following reasons:a. Model file "cifar10_weights.h5" used by this program is wrong?b. Accuracy of training program that generated this model file is very low?Questions are:10a. Which is the traing program that generated the above model file?10b. Is it the program on pages 128 and 129?11a. Program on page 129 is saving to "model.h5" file11b. I ran the program on page 129 and renamed the model file "model.h5" as "cifar10_weights.h5"12a. Then I ran program on page 131 and getting following error: ValueError: You are trying to load a weight file containing 13 layers into a model with 6 layers.12b. Authors need to fix these errors please?12c. Fix model file names of programs on pages 129 and 131 please?Thank you.Jan 24, 2020 Review Notes:Publisher of this book has stated on this book's Github web page, that the corrections to source code files will be made in few days and posted to Github.This is an excellent and very well written book and is filled with essential information about deep learning concepts and programming techniques.This is a must to have book for persons working with Python and Deep Learning.Thanks and best regards,Jan 20, 2020 Review NotesAuthors have not updated code files from Chapter 4 per my notes 1 to 6 below:They added some missing image files to chapter 4 folder.8. Many programs from Chapter 4 are giving errors and not running.Author seems to have installed older versions of Python tools few years back when they started writing the book.Now many of the Python tools have new versions and have deprecated or removed features. Therefore many of this book's programs are not working with newer versions of Python tools.9. Therefore, authors need to install latest versions of all the software tools on a clean new computer and test all the programs and update github web page with the source files that can be run using latest software tools versions please. Thanks.Jan 19, 2020 Review NotesAs per my notes 1 to 6 below, authors have quickly posted within few days,Python Source code (missing or corrected) of Chapters 4 and 5 to the Github webpage of this book. This is great, thanks to the authors. I had only print edition of this book so far. Now I bought Amazon Kindle Fire edition of this book also.I request one more suggestion (note # 7) to the authors:7. Please rename each source code file by prefixing with pgXXX_ corresponding toapproximate page number of the code.For example, for page 115, pg115_leNet_CNN_mnist.pyThis kind of renumbered file names will help readers of this book,easier and quicker to find source code file and vice versa.If a source code file is discussed on multiple pages, then the file name needs to bepg115_116_117_leNet_CNN_mnist.pyIf authors can quickly rename the source code files and post updated source code file names to Github,then I will upgrade stars of this book from four stars to five starts please. ThanksFollowing notes are regarding second edition of this book:1. Chapter 5 python source code files are missing on Github download webpage2. Many programs in Chapter 4 are not working and giving many run-time errors3. Python program on page 131 is not working4. Image files are missing (in github downloaded zip file)5. On page 131 program, getting cifar10_architecture.json can't be opened error6. On page 131 getting error with .astypeMany other python programs in the book are not workingAuthors are requested to quickly fix these errors and upload corrected programs and updated readme listing corrections made to the downloading zip file on the github download website please.Thanks and best regards,Authors of this book have quickly made changes within few days, per my above notes 1 to 6, and postedcorrected/missing code files to the Github. Thanks to the authors for quick fixes.

I was new to machine learning when I tried to read this textbook. I think it would be better suited for someone who has already been studying machine learning for a while. I have a lot of python experience though, so that made things a little easier. I like that it tries to cover a ton of different things. The best thing about this book is that it covers a wide range of topics. But I feel like each topic in the book is explained very quickly. I had a lot of questions about each topic and had to Google a lot of stuff while reading it. It wasn't always clear why they were adding the layers that they were adding to the model. Or what the advantages and disadvantages of doing it that way was. If this book was a class, I probably wouldn't do so well on the final. Maybe someday I will get good enough at machine learning to be able to help the authors edit and improve the book. I do feel bad giving it a 2 star review because I can tell a lot of time and effort when to writing about this many different topics.

This book has no details about Keras code, just broad strokes and mathematically meaningless explanations.Over 600 pages essay on deep learning for children. This is a classic case of publishing for the sake of profit.

Really shallow content. Just filling in pages for no real content or information.

I started reading the book few weeks ago. I must say it is lovely and nicely written. It is easier for to read it after being in touch with Keras, fastai(build on top pytorch). Of course, with some machine learning background things can go smoothly. My recommandation would be to dig in well the first chapter as it has the base concepts of machine learning. I do recommend it! And I love it!

A useful book, especially for people who don't have rich coding experience

As a deep learning practitioner in the computer vision space, I found the academic content in Chapter 8 RNNs, Ch. 9 AutoEncoders, Ch. 11 Reinforcement Learning, Ch. 15 The Math behind Deep Learning and Ch. 12 TFX to be most useful. Of course, the other chapters are useful as well, and do tie into the ones I just mentioned. But as a professional in the field of machine learning, I got the most lift in the chapters I mentioned above. These topics will help you get a data science job or develop your current skill set if you’re already in the field:RNNs: Excellent review for those of us that work with them, and it does touch on some advanced topics like transformers, which are SOTA as of 2020.Reinforcement Learning: It goes into depth on Q-Networks and deep deterministic gradient policies, so a data scientist will understand these better when implementing from outside code tutorial -- very useful for this.Auto Encoders: Lots of business use cases can take advantage of these, so I recommend taking the time to learn this, and code them out.Math Behind Deep Learning: More for beginners entering the deep learning field, however, for experienced data scientists it wouldn't hurt to go over derivatives, differentiation, chain rule and backpropagation.TFX: A little light, it’s more of a detailed outline, I wish there was a docker-compose and coding example on how to set it up and connect TFX Pipe to TF/Keras models. External investigation is needed to develop this skill set, as getting models to prod are crucial.

I have been following the work of the authors (especially Amita and Antonio) for many years - and we have used Amita's previous book in our course at Oxford University. This book extends that thinking which the authors have been building up over the years. It is a good reference. The book comprehensively covers coding on Python for machine learning and deeplearning with an emphasis on tensorflow 2.0. The book also covers some extra topics which make it a great reference such as: Tensorflow in the Cloud; IoT and AutoML

The authors' mastery over the subject matter is clear by the way they can take a complex subject like this and make it easy. This book achieves just the right balance between theory and practice to maximize real-world learning. I learn much faster by getting into the subject with hands on examples, so it was a huge help that the examples in the book were easy to reproduce without error.There are some other good books on TensorFlow and Deep Learning, of course, but in my opinion, this is the best one. If you're looking to learn about Deep Learning with the latest technologies out there, you'd do well to start with this one. Depending on how far you go with it, it may well be the only book you need.

This is a great book that explains in a very approachable way complex ML concepts. You must have a basic understanding of ML and I would recommend this book to anyone who needs a comprehensive guide for Tensorflow and Keras. The book contains as well some of the latest techniques such as capsules and much more. The authors have really touched upon a variety of topics and will help the reader navigate through the numerous algos available today.

enjoyed reading

The book covers almost of of the deeplearning topics with example codes. This is a comprehensive guide for machine learning professionals to use tensorflow and keras for deeplearning problems.