Deep Learning with TensorFlow 2 and Keras

Colour edition would have been much better for the price, as it gets bored to study this black and while, we are dealing with complex subject like deep learning, image classification n all

A very good introduction. A few typographic errors but fairly obvious to spot. Most annoying is the use of acronym without definiton such as Self Driving Car (SDC). I had to look up ontology but generally the authors describe the concepts and algoritms extremely well. Chapter 10 unsupervised learning covers PCA,K-NN and RBM and uses mathematical terms which may be unfamilar. I think a section on Bayes and PDF could be added to the variation autoencoder section (or the mathematics chapter). The best thing is lots of coding examples. For an absolute introduction I still recommend Deep Learning for Coder with fastai and Pytorch but if you have a basic knowledge this book is wider in content and great value.

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.

The key is in the book’s title: flow. Yes, that’s my very own (100% bio/natural ;-) ) neural network eventually got to when trying to concisely describe this book. Given the non-triviality of the topics that the authors wrote about, that alone is a remarkable outcome IMHO. There’s a subtle though absolutely pragmatic approach in every chapter that guides the reader’s reasoning to a double win: grasping the inner value of the core concepts and quickly gaining real world examples (through code). I also found the vast majority of chapters to be almost ‘self consistent’: although some cornerstones are required (and thoroughly dealt with in the first few chapters) you’ll find yourself jumping back straight to, say, GANs or AutoML focused chapters for future reference or deeper dives. The ‘math focused’ chapter is an added bonus which, although not stricty necessary for the book’s mission, deserves its own credit and will give you some extra ‘Ah!’ moments.

Es un libro que contiene todos los aspectos que más se usan del DL. Además da explicaciones claras y profundas. Aunque para mí lo más importante y valioso son los consejos que dan para solucionar problemas cotidianos del machine/deep learning.