Introduction

I started my masters thesis work on August 2017. My work was on machine learning. My instructor immediately gave the deep learning book to read. After studying it for four months, I couldn't be more thankful to him. This is one of must read books if you want to get started with deep learning.

Why? You may ask. I am here to answer that exact question. I am writing here an indepth review of the book, so that you can be clear on whether to read this book or not.

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Before reading this book, I had done courses on Linear Algebra, Calculus and Statistics and for me, the first five chapters were a re-organized summary for what I'd learned . The first five chapters cover all the basics necessary. For a person who is starting with machine learning, he/she might get overwhelmed by the details. This book is written in a very formal and plane manner and also you feel captivated enough to follow through.

It efficently reviews the current models of deep learning. It makes a big jump into more detailed and technical matters, so it can be sometimes intimidating for a non mathy person. I can see this being an excellent text for an MS or PhD course. Personally, I feel Neural networks are poorly-understood beasts and therefore, the state of research around them can be chaotic and overwhelming.

One gets the sense that mastery of deep learning is achieved more from accruing an encyclopedic knowledge of special tricks and corner cases rather than any grand or cohesive theory.

This book ties such a vast and disparate amount of knowledge together in a single high-quality package - that's the really impressive part.

This book made me realize that fundamentally optimization is the king. After reading this book, I worked on solving multi objective optimization problems using genetic algorithms, read papers related to genetic programming and I didn't feel hard in reading those stuff thanks to this book for making my basic concepts rock solid.

I really thank the authors of this book for bringing in the passion to pursue this field. I just had to write the review, it was that awesome.

You can read the book I am talking about here.

I also write top ten data science articles to read every fortnightly. Follow to get updated and stay with the trend. This is the first part of my series and this is the second part of my series.

Thanks for reading! Do read this book if you are planning a career in machine learning!