Math for Deep Learning: What You Need to Know to Understand Neural Networks

I have read several math books for ML and AI. This one surprise me in many ways. Complex concepts made easy to learn, with code examples and use cases. I really recommend this book for begginers.

Dr kneusel does an amazing job breaking down neural networks into the fundamental components as well as statistical frameworks . Highly recommend for anyone looking for an intro to or refresher on the math required for ML. It will require some studying though if one does not have a solid maths background .

This book is easier to understand and for all samples it uses code the thing which we are going to use on daily life.

I've skimmed the entire book and have worked through several chapters in detail. My first reaction is this book covers about 3-4 years of undergraduate math with some graduate school math topics. My CS degree is 30+ years old, but my daughter just finished her CS/Math degree and the undergraduate math requirements have changed very little. My old-school CS degree covered only about 80% of the topics in this book. If you've taken these classes then this is book provides an awesome review put in the perspective of how to use python to help with these problems. If you haven't taken at least statistics, calculus, and linear algebra, you may find that you need books that introduce these topics in a more beginner-friendly way. And then use this book to provide ML context to the math. There are many good intro books and youtube videos that can help start with these topics. An example of a beginner-friendly statistics book might be "The Cartoon Guide to Statistics" which is fantastic. Good videos would be anything by 3blue1brown. I'm reading the Kindle version of the book. The formatting seems quite good for a Kindle book. Kudos to the author for not treating Kindle as an afterthought. I'm finding this to be a very useful book. The title did warn that it's a mathy book. I think to get a good feel of ML right now, this math background is probably necessary. And this book seems quite good.

This is a Math Book, but does not have anything applicable to Deep Learning! The author spends three pages on the Monty Hall Problem (For those who don't know what the Monty Hall problem is, it was a TV show thirty years ago, called, "Let's Make a Deal".) What has the Monty Hall problem to do with Deep Learning. It would have been useful if the author had taken a single example, say, the recognition of hand-written digits (the famous MNIST), and worked from the beginning to the end, including the back propagation, and introducing the necessary Math as he proceeds, it would have been immensely useful. I can think of at least one use for this book: Use it as a paper weight.