5 Deep Learning Books that ought to be present in your Reading List
‘Deep’ puts forth the notion and depth that the term Deep Learning carries. It has sprung up in the 1940s and has remained here ever since. Its success is related to its closeness to the functioning of the “human brain”.
Humans’ have always been the suave and craziest social animals on Earth. Unlike others, they are capable of thinking and decision making in the smartest way possible. Deep Learning revolves around human brain imitation and intuition, and it henceforth, gained a lot of momentum.
Understanding the core concepts is quite essential to get a hang of what Deep Learning encompasses and teaches. Amongst a horde of books available today, choosing the right set of books is crucial.
When I started learning and practicing Deep Learning, I wasn’t sure of the learning path that I have to choose. The concepts were quite confusing and I rummaged through the web to get to understand how Deep Learning actually works. Referring more books is definitely a good choice, nonetheless, having a curated set shall help you in thoroughly understanding the concepts.
Here are a few books that caught my attention in particular.
I hope you are going to gain a deeper understanding reading these.
1. grokking Deep Learning
If you’re a beginner, I would strongly recommend you to start your learning with ‘grokking Deep Learning’. Neat illustrations and clear conceptualization shall help in deepening your interest towards Deep Learning. This book isn’t framework dependent as code snippets are written using pure Python and NumPy — an added advantage for you to dive into the basics.
2. Deep Learning
Deep Learning is tightly bound to Mathematics. This intuition is clearly explained in ‘Deep Learning’. Written by three renowned researchers, this book is everything from mathematics to the modern practices that have evolved over time in Deep Learning.
I would highly recommend this book if you want to gain a deeper knowledge of the concepts. That saying, it’s completely theoretical; yet every page in it piques your interest as to how Deep Learning has unfolded.
3. TensorFlow for Deep Learning
If you are into bringing Deep Learning into the real world by coding using TensorFlow, this book is your best bet. I assure you that you would understand the nuts and bolts of TensorFlow framework reading this. The authors give a thorough explanation of every TensorFlow operation that you could possibly implement in order to understand Deep Learning.
4. Deep Learning with Python
This book shall keep you hooked till the end owing to its conciseness and simplicity. Both theory and code go hand-in-hand. Keras API (built on top of TensorFlow) has been used to explain the code concepts.
If you do not have the slightest notion of what Deep Learning encompasses and if you would want to code by learning, this book should be your savior.
5. Deep Learning: A Visual Approach
Right from Probability and Bayes Rule to Generative Adversarial Networks, ‘Deep Learning: A Visual Approach’ clearly elaborates every concept with visual representations. It simplifies the internals without making the learning complex.
If you’re a visual person yourself and if you want Deep Learning to be explained in simple terms, this book should help you.
