Are you ready for the best TensorFlow 2.0 book of 2019? We have got a lot of long lists on the internet with titles like “21 Best TensorFlow Books you Absolutely HAVE TO Read” lately. To be honest though, one rarely have time to read 21 TensorFlow books. This article will therefore focus on THE one and only most amazing TensorFlow 2.0 book of this year. Are you excited yet? We sure are!
Introduction to TensorFlow 2.0
TensorFlow 2.0 is an upgraded version of Google’s extremely popular deep learning library TensorFlow (TF). The idea is to make TF developers more productive and efficient. Some of the main changes from the original is that 2.0 has removed redundant APIs, made several APIs more consistent (Unified RNNs, Unified Optimizers) and, perhaps most noteably, highly improves the Python runtime integration. If you wanna learn more about TensorFlow, feel free to look at this neural network example in TensorFlow.
Prior to this article, we’ve studied almost all books and articles about the fairly new TensorFlow 2.0. The result of this study is that we can now present you with the absolute best book alternative if you are looking to learn.
The Best Book for Learning TensorFlow 2.0
Okay, it’s time to reveal our #1 best book for TensorFlow 2.0. The winner is the brilliant Hands-On Computer Vision with TensorFlow 2 by Benjamin Planche.
The full title of the book is Hands-On Computer Vision with TensorFlow 2: Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras. Much like the name suggests, the main focus Hands-On Computer Vision with TensorFlow 2 is image processing and image manipulation. Don’t worry though, most of the material taught in the book can easily be extended to and applied in other deep learning areas such as natural language processing and speech recognition.
What makes the book so good and interesting is the fact that the examples presented often relate directly to real-life challenges. The mathematical background required for understanding neural networks and how they work is clearly outlined. The book uses historical image processings riddles and illustrations to explain the theory behind the material without oversimplifying things.
The book doesn’t require you to know any programming, but some Python knowledge will definitely help you get started.
Topics of Hands-On Computer Vision with TensorFlow 2
Here is a list of the content topics you will find in book:
- Image classification with state-of-the-art architectures (including Inception and ResNet)
- Building neural networks in TensorFlow 2.0 from scratch
- Video analysis using recurrent neural networks
- Object detection with YOLO, U-Net and Mask R-CNN
- Performance optimization with transfer learning, domain adaptation, and GANs
- Deploying TensorFlow 2.0 models in Apps and on websites
- In-depth guides to developing self-driving cars and facial recognition systems
And much more!
What Makes Hands-On Computer Vision with TensorFlow 2 THE Best Book
But what makes this book the best one out there today? First of all, it is well written, clear and spot on. Secondly, you will learn ALOT from reading it. Not only about TensorFlow 2.0, but about machine learning, deep learning and artificial intelligence in general. The book will take you through some of the most important (and most exciting) areas of field and keep you glued to the pages.
Other Amazing TensorFlow 2.0 Books: Honorable Mentions
It is always hard to pick a #1 without something being left out. If you already have worked a lot with the original TensorFlow, we’re convinced that you will love What’s New in TensorFlow 2.0 by Ajay Baranwal. The book does an amazing job at clarifying everything that’s new and cool about TensorFlow 2.0, and the subtitle of the book is Use the new and improved features of TensorFlow to enhance machine learning and deep learning.
Do you agree with our choice? Or do you have a better suggestion for the best book for learning TensorFlow 2.0? Don’t hesitate to let us know!