books and courses

What’s up guys?

In this article, we’ll talk about how to get started learning about deep learning and machine learning. Here will be a selection of books and courses (free) for your quick and easy start. I suggest not to hesitate and start right away.

Books

For newbies:

Grocking Deep Learning – Andrew Trask

This book, in my opinion, is not a bad starting platform for learning and understanding DL. After reading this book alone, you will certainly not become a deep learning professional, but you will know and understand the basics and how to apply them. The only thing is that the author offers code examples in Python (the book is based on this), but there are many errors in the code from the book, so you need to be more careful and also, you need to know the basics of Python. But overall, this is truly one of the best books to start with.

Creating a neural network – Tariq Rashid

This is another book from the “for beginners” category. In practice, it will help you create your own neural network for image classification and explain how this network works. The first part of the book is theory, the second is practice. The book is not bad for understanding the basics – I recommend reading it if you are a beginner. Oh, yes, the book also uses Python, but I didn’t find many errors there.

For non-newbies:

Deep Learning – Aaron Courville, Joshua Bengio, and Ian Goodfellow

In my (and not only) opinion, this is truly the best book where all the basics of deep learning are outlined in detail. In addition to specifically deep learning, the book will also help you with the necessary mathematics, but to read it most effectively, I recommend studying at least the basics of linear algebra, probability theory and some other topics (all this theoretical basis is in part 1), but the authors leave links to their recommended literature. I can talk for a long time about how good this book is, I won’t do that, but I’ll just say that if you want to delve into the basics of DL, then this book is for you.

Neural Networks – Saiman Haiknin

In general, this book is not similar to the previous one, it also outlines the basics of neural networks, but here you will definitely need good knowledge of mathematics. If you look at it in general, this book perfectly complements and expands the previous one.

Books about Frameworks

I also need to say about two books about Deep Learning Frameworks for Python:

• PyTorch. Lighting DL – Eli Stevens, Luca Antiga, Thomas Wiman – this book, as the title suggests, is dedicated to the PyTorch Framework.

• Deep Learning in Python – Francois Chollet – and in this book we are talking about the Keras and TensorFlow libraries.

Courses

When I talk about courses, I mean free courses:

• CS50’s Introduction to Artificial Intelligence with Python – this course is not only about neural networks, but, as the name suggests, about the basics of AI, but there is also a certain amount of information about neural networks (by the way, keep in mind that the course is from Harvard and it is entirely in English, but there are subtitles for the video ).

• Machine Learning with Python: from Linear Models to Deep Learning – course about ML from MIT (also in English).

• Deep Learning with Python and PyTorch – a course from IBM about deep learning and working with PyTorch.

In general, this is all for the courses, I have included here only those courses that I consider really good and that will help you gain the necessary knowledge.

Adviсe

And now I would like to give some tips regarding studying this topic:

• Start simple. If you are new to this topic, don’t jump over your head – start simple (Grokai’s book Deep Learning is a great place to start).

• You don’t have to read a book or watch a course cover to cover. You don’t have to read everything at once. You can skip something and come back later when necessary.

• Practice. Be sure to create your own projects. Practice is often much more valuable than theory.

• Don’t wonder. Don’t give up, even if your progress is not visible or you don’t understand anything – don’t give up, keep working.

• Find friends or a community with similar interests. This will help you develop faster.

If I forgot something or made a mistake somewhere, write in the comments or PM.

From the author:

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Good luck!