Clear, structured, top

I thoroughly enjoyed Raschka’s book. It explored a variety of topics on machine learning and deep learning. Some of it was familiar but more than half of it was new knowledge. It expanded my knowledge on the cutting edge methods. Also I liked that all chapters had exercises. If you’re looking for something like this, I highly recommend checking it out.

I found this to be a very well-written, accessible, helpful tool in going deeper in understanding AI and the concepts around it. Even beginners can get plenty out of it, although it's primarily geared toward those with a solid foundation already as others have pointed out.

Being an avid reader of Raschka's excellent Ahead-of-AI newsletter, I was thrilled to find his physical book on the shelf. It's quite pricey, so I really checked out the content before buying but what a gem! Each little "Question" chapter is short, to the point, eminently readable and really clarifies the underlying concepts clearly. So glad I bought it. If you're navigating the space of ML & AI and wanting to understand the technical details but you don't have a strong computer science background, then this is an ideal resource and must-have! And you don't have to read the chapters in order - most of them can be read on their own.

I am a software developer and was excited to find out more about AI programming. This book clearly assumes a very high level of existing understanding of math and core AI concepts. Literarily the first concept gets into "high-dimensional" data and "low-dimensional" vectors. The title is misleading because for a novice an essential question would be "what are low-dimensional" vectors, what is convolution, etc.

In general, the book assumes a large base of knowledge from the reader and your knowledge would have to conform to the terminology used by the author. Each chapter tries to answer a particular question, but without acontext, it's hard to understand how the answer would be used. The book would be vastly more useful with a context example for each question and definitions of important terminology. The complicated figures for neural nets could use a explanation paragraph to understand the conventions used. Finally, the exercises at the end of each chapter seem impossible to answer with the information that was given. Instead, it'd be better to add them to the chapter as related questions with the answers that are in the back of the book part of the chapter as opposed to an appendix to the book.

Répond 1 mes attentes. Super livre pour s'approprier les concepts de l'IA et du machine learning.