Practical Deep Learning with Python: A Hands-On Introduction: A Python-Based Introduction Paperback – 25 Feb. 2021

All technical writers should take notes from Kneusel! He provides a highly approachable introduction to the subject of machine learning, which thoroughly covers all fundamentals without sacrificing technical depth. It provides all of the necessary context to truly understand the subject matter and presents it in a logical progression that helps the reader build a sense of mastery. Highly recommend for engineers and hobbyists alike!

Last year I started my first project assignment on deep learning, and puzzled my way through it using Stack Overflow. It was a disjointed and somewhat awkward way to learn, but I did in fact get a lot of the basics. However, when I saw this book I suddenly realized it was past time to do some proper reading and learning on the topic, and fired off an order. Reading it now gives a series of 'oh yeah', and 'aha' moments, as it connects up various loose ends I'd come across and never properly registered. It's written in a semi-conversational style that is similar to how you'd get information from a co-worker, if you had a co-worker that was knowledgeable in deep learning. For example, it has a style where there's an occasional contra-example given to show what something ISN'T, alongside what it IS. I didn't have any such co-worker, but if I'd had this book instead of just stack overflow it would have been a big help. Highly recommended if your learning style is anecdotal, informal and driven by hands-on results.

Excelente material, sumamente pedagógico, bien estructurado, claro y conciso. La vara de entrada es saber programar en Python y conocer los fundamentos de NumPy.

Most books on deep learning start simply enough but quickly turn into graduate-level math textbooks after a chapter or two. Ron created a true practical guidebook that will get you running sophisticated analysis without all of the theoretical background. Code examples produce graphical output and simple statistics that help the reader understand what they are doing and develop the intuition to compare different methods.

I bought this book hoping to learn Deep Learning.Unfortunately I found this book to be Neither Practical nor useful.I own this author's both the books.Both of them are duds.The author merely starts the applications and leave them there without taking even one of them from beginning to end. This does not help.Any author who writes a book on Deep Learning should take at least one application (PLEASE leave out the IRIS data set!) from beginning to end to benefit the readers who pay a lot of money.What this author provides in his book is easily available online for free.