Bellow a curated list of books which I consumed in the recent past years, which my thoughts on them.
Machine Learning and Data Science books
TensorFlow for deep learning : from linear regression to reinforcement learning (Ramsundar and Zadeh 2018)
Overall is a subpar hello world kind of book for
Tensorflow, nothing but the
basics. Chapter 8 on Reinforcement Learning has some nice examples which are
easy to be understood.
Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks (Galeone 2019)
Is a book from Packt but besides the bad reputation from this publisher, the
books is quite good, it touches some more practical subjects, as how to convert
a model to
Tensorflow.js and some practical examples of using Generative
Adversarial Networks. Besides other Packt books where chapters usually don’t
maintain a context or narrative in between them, this book focus solely on image
Bug Bounty Hunting Essentials : Quick-paced guide to help white-hat hackers get through bug bounty programs (Lozano and Amir 2018)
Chapter on Basics of Bug Bounty Hunting can be skipped, just generic information which you could do better by reading the official information of the platform of your choice. The chapter How to Write a Bug Bounty Report lacks real content and better examples.
The only interesting point about the book starts on the chapters dedicated to specific vulnerabilities, in which are discussed in details some vulnerabilities found on famous services, like Uber, Shopify, Binary.com among others.
Programming with MicroPython : embedded programming with microcontrollers and Python (Tollervey 2017)
Very high level view, basically a book with some hello world examples which
didn’t add any value. Lacks the full view and workflow of using such devices, it
focused mostly on