Practical Guide to TensorFlow for AI and Machine Learning

Met and exceeded my expectations. Great walkthroughs and explanations on coding machine learning into different environment (using TensorFlow). Also good starter discussion on ML techniques. I especially liked the mx+b example of neural networks, very intuitive. Answered lots of questions I had about using my models, such as with JavaScript or using transfer learning.

The last chapter on ethics / federated learning felt a little short (but the fact it was there was already good). I would have liked a little more on dealing with / identifying model drift and training on new data while not overwriting the weights of the old. Essentially more lifecycle stuff. Discussion of ML model deployment was all TensorFlow but like I really enjoyed it all.

Likes:
- It is a good intro for student of AI and machine learning.
- It helps you get a grasp on machine learning quickly. If you want to learn by doing, this is the right book for you.

Dislikes
- The author does not always hold you hand through the whole journety. You have to figure out certain things for yourself by looking at the example code which is available online. As the title suggests, the book is for coders who know how to read code and put together pieces of code snippets to make the examples work (but don't worry, it's not that difficult).
- The author does not spend a whole lot of time on explaining the details of fundamental AI/ML concepts. Again, if your background is not in engineering/math/programming, you may find it hard to keep up. You might want to learn the basic concepts elsewhere first.

All in all, this is a good intro book on AI and machine learning, but you will most likely need supplemental material to have better understanding of what's in the book. I personally prefer the teaching style of pyimagesearch.com and it university course which is more hands on and takes time explaining the difficult concepts.

I found this book to be incredibly practical, easy-to-approach, and directly applicable to real-world problems that I am trying to solve. I have read quite a few Machine Learning (ML) and AI-type books "for programmers". This book differs from them in having the sole focus of giving programmers the minimum, pertinent, focused information and context needed to start utilizing TensorFlow (TF) on their own projects, right now. There are enough examples in this book that one of them might directly address a problem you are contemplating using TF for. If not, reading how to apply TF across these various scenarios is likely to enable you to try TF in your project. I really like the "results-driven" organization of the chapters. This makes the book both a self-contained class in using TensorFlow as a developer, and a good "cookbook" for both using TensorFlow, and deploying TensorFlow models to alternate/resource-constrained devices (via TensorFlow Lite). I heartily recommend this book for these purposes!

Laurence is the teacher you always wish you had. He covers every aspect of TensorFlow with deep yet tangible intelligence. If you're worried about understanding the practice of machine learning and artificial intelligence, don't worry any longer. This book guides you through various aspects of the TensorFlow framework, but it also leans into the varying aspects of popular AI problems.

You get the full tour in this book, from NLP coding to embedding your models in edge devices. Each section is overflowing with code samples to help you solidify what you're learning.

10 out of 10, a cornerstone of any AI bookshelf.

Laurence explains tough concept in easy way.
Code in Github and website of Laurence has good examples
This is my 3rd book on Machine Learning, and I have seen different approaches to explaining complex concepts like How CNN works? etc. The stye used by Laurence comes with many decades of experience to understand and I guess thats why he can explain so well. I am liking it :).
I just finished Chapters 1, 2, and 3 with coding along the way.
Will update again as I read and practice further.
Thanks Laurence Moroney.
Mahtab Syed
Melbourne

If you start with this book, watch Laurence's videos on the TensorFlow/ Google Developer youtube channel and work all the examples and exercises, you will have a solid foundation in deep learning. Then you could potentially learn more from the documentation itself or by using Aurélien Géron's book.
I feel other Oreilly books should be like this.
Although math is almost absent in this book (hence 'for coders'), Laurence does a great job explaining convolutions and maxpooling etc.. I've seen lectures from many sources that misattribute pixel/dimension reduction to the convolution step and not the pooling layer (worse yet, lump both together) but the author clearly explains the concepts.