Deep Learning: A Visual Approach

I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.

Diving into the world of artificial intelligence can feel like stepping into a vast, uncharted ocean, and if you're looking for a reliable vessel to navigate these waters, this book is an excellent choice. However, I must be candid—this journey is not for the faint-hearted or those hoping to breeze through. The subject of AI, with its complex algorithms and intricate theories, is notoriously challenging. You won't find yourself flipping pages at a rapid pace, as this is not a title designed for speed-reading. Instead, it demands your full attention and a willingness to engage deeply with the material. At the heart of AI lies mathematics—a fundamental pillar that underpins the entire discipline. This book, while comprehensive, offers only a glimpse into the mathematical framework that drives artificial intelligence. But don’t be disheartened by this. Think of it as a solid foundation, a primer that will arm you with the essential concepts needed before you delve deeper into the more advanced mathematical intricacies elsewhere. When you do eventually tackle those more complex equations, you'll find yourself better equipped, with a clearer understanding of the principles at play. I should also mention that I'm no stranger to Andrew's work. Having explored some of his other writings, I can confidently say that he possesses a unique flair for communication. His ability to distill complex ideas into accessible language, without losing the essence of the subject, is truly commendable. Andrew writes with a certain finesse and sophistication that makes even the most daunting topics seem approachable. His style is not just informative, but also engaging, with a touch of elegance that sets his work apart from others in the field. In summary, while the path to mastering AI is undeniably steep, this book serves as an invaluable guide. It’s not just a starting point; it’s a beacon for those who are serious about understanding the intricacies of artificial intelligence. Be prepared to invest time and effort, and in return, you'll gain a solid foothold in a subject that is as fascinating as it is complex.

Deep Learning is changing our world. If you want to understand more, this is a great place to start. Andrew Glassner is a talented explainer - I took his short course on Deep Learning and learned so much, but also came away impressed at how well he can make complex material so clear and engaging. And this book is jammed packed with insights, visuals, and clear explanations. The author has a playful, sometimes quirky style that shines through, which gives this tour a lot of personality as well as information. Very enjoyable reading - I felt like he captured all that was good about his course (and then some) and bottled it up in this book. There is a lot more material here than in that course, and it is well laid-out and organized so that it is easy to roam around and come back to review the pieces that matter to you. Even if you plan to go to on to be a world-class Deep Learning engineer or mathematician, you have to start by understanding the concepts. And this book does a great job of presenting all the core ideas in a way that makes them clear and memorable.

One of the most comprehensive guides to AI and deep learning available. All concepts covered clearly and concisely with illustrations. Complex concepts are broken down into understandable terms. Even though ML and AI require some complex math, you won’t need it to get idea of what’s going on inside the computer’s “brain”. I use this as a reference when teaching AI concepts and preparing presentations. I highly recommend it

Excellent book. Highly recommend

Amazing book. Great examples and diagrams. If you're looking to get an intuitive grasp of deep learning, look no further. If you're an engineer looking to apply it, I would recommend pairing this with one of the more technical canonical texts and a programming focused book.

I'm finding this book a great reference to supplement Syracuse IST 691 Deep Learning in Practice. In particular, its explanation of backpropagation is the clearest I have found yet, and that even includes 3 Blue 1 Brown, which always does excellent explanations.

A good narrative description of how the various systems work. Good for getting a conceptual understanding. No math to speak of, and if you want it, you can refer to the references cited, or your other favorite machine learning book.

What a wonderful book to explain deep learning. Andrew does an impressive job of explaining concepts in an intuitive way and fully supported by extensive code to help get upto speed quickly.

I was hoping the book would explain or give a visual presentation of the process of calculation for any deep learning problem, however it only shows the end results, something that many books would show as well. I was hoping the attention architecture would be more visual, however it isn't.

Given the tools already available to implement Deep Learning models, this book takes a visual illustrative approach to fast track one into this research field. It contains great figures and excellent examples with intuitive approach to state of the art topics. I am using this book to teach Deep Learning this semester. The companion website is very helpful. The clarity in explanations and very insightful examples make this book a treat to read. This book is unique and indispensable for novice in the area of Deep Learning. Highly Recommended.

The pages are crumbled and the replacement has scratches covers. Both has black soot stains.

If you want to understand core deep learning concepts think no more. This is your brain friendly guide