Neural Networks and Deep Learning: A Textbook

I have familiarity with data mining concepts and general machine learning. I am a practitioner of Machine Learning and am very interested in applying these models to real world problems. The purpose of buying this book was two fold: 1. I wanted to get an understanding of deep learning - how neural networks work and how they can be put to use and 2. How do neural networks compare in relation to other conventional machine learning models? How are they related and where is their place in the field of machine learning?From either point of view, I feel that every penny I spent on buying the book is worth more than its weight in gold. This book starts with a fairly detailed introduction into simple neural networks. The early chapters establish crucial and very useful connections between conventional machine learning methods and how neural networks can be built to mimic them. Ample examples and details are given to walk the user through intricate scenarios. Example, there is a whole section which unboxes gradient descent and explains the math behind it. There are several places in the book where connections are drawn between neural networks and how they simulate linear regression, logistic regression and SVMs. Several variants and differences are also explained in great detail. Once these are established, early development in neural networks are addressed - Radial Basis Functions and Restricted Boltzmann Machines are discussed in depth. After setting the fundamentals, the author goes on to address topics in deep learning - starting with RNNs, CNNs, Deep Reinforcement Learning and more advanced topics like GANs.The book also provides and cites ample references which inform the user about the historical progress and development of the field. The references have been compiled with great care and so are the diagrams. Very detailed explanations are provided to connect practicality of the methods. For instance, for activation functions, several examples are provided based on what functions are used in practice and how the choice impacts the complexity of models and what conventional ML models they map to.A more detailed review will follow as I progress more through the book but for starters, this is a great book to buy - be it for reference, or teaching a course or for getting to know the field. If you have experience in ML, you will definitely benefit from the insightful connections of neural networks with conventional ML methods. For teaching, the accompanying web page has a wealth of resources in the form of slides, Image sources for pictures in the book to compose your own slides and other files accompanying the book. Definite buy to have in your shelf if you are interested in Deep Learning.

I've truly enjoyed this book. I don't have a mathematical background, so some machine learning techniques can be difficult to understand without a lot of serious effort. I haven't had that problem with this book. It explains how various neural networks work at a conceptual level, which is a must-have for anyone considering doing serious work in the field. Even though it's math intensive, I found it very easy to understand and the figures were incredibly helpful in piecing everything together. It is also very comprehensive. For the past year, I have been doing survey research in the field and this book is thorough: it goes in detail on every major model and advancement.Just keep in mind that this is not a technical how-to, it focuses mainly on conceptual understanding.It is not easy to simplify a complex and difficult field in such a well-thought out way. I'd recommend it to researchers, students and everyone else interested in deep learning.

This is modern, comprehensive text on this rapidly evolving topic. There are some good exercises and a very large, up to date and valuable bibliography, including many links to useful websites. Unlike many texts on the subject, mathematical details are included. The author must have had extensive practical experience in constructing, training and deploying neural networks as well as using deep learning techniques. The book is peppered with interesting biographical and historical comments. I think that it is an excellent reference work for an instructor or advanced student. However, the reader should be aware that there are no computer scripts or even pseudocode. Of course, including code would significantly enlarge the already large book. But I suspect that a beginner might not be able to construct a neural network using this book alone. With this caveat I recommend this well-written book.

First of all, this book is very Mathematically grounded , but the author has provided the best explanation yet of Backpropagation using Dynamic Programming approach. I personally loved the chapters on Computational Graphs , Multilayer Perceptrons, Backpropagation and Modern Architectures like Auto-Encoders , GANs, this book also gives the necessary grounding to tackle the research papers and some of the exercises in PyTorch are pretty good if one wants to get their hands dirty

Finally, we have a book that combines intuition and mathematics todescribe the analytical and methodological aspects of deep learning.The writing style makes it easy to follow complex material.This type of approach is needed for true mastery of the subject.In this respect, it is probably a great complement to implementation-style books,because academic books have a focus on fundamentals rather than implementationframeworks. The book also provides intuition on how neural networks can be usedin many real-world applications. In that sense, I do not think thatthe utility of this book is restricted to academia.

This is a fantastic book from the academic perspective, and hasquite a bit for practitioners too in terms of conceptual understanding.Considering the fact that it is a mathematically intensive book,it is relatively easy to understand. Not an implementation book, but great fordeeply understanding concepts. The book has managed to providediscussions of the architecture of lots of real-world applications of neuralnetworks in text, images, among others, which is good forpractitioners. Certainly, hands down better than the Goodfellow book,the only other directly comparable book out there in terms of styleand material covered.

Este libro es muy bueno. Lo que más me llamo la atención fue la cantidad de ecuaciones y su explicación. Son pocos los libros de redes neuronales y aprendizaje automático que maneja ecuaciones y las explica, dejando ver como se aplicarían en los programas. En algunas ocasiones no profundiza en algunos temas, dejando muchas preguntas al aire, pero lo compensa al ir directo al punto clave del tema. Sin embargo, cabe mencionar que requieres de conocimientos previos de matemáticas y teoría de computación, por lo que no es para cualquiera. Lo recomiendo si te gusta todo lo relacionado con la inteligencia artificial y las redes neuronales.

So machine learning books usually fall into one of two categories:- hardcore academic, like Ian Goodfellow's "Deep Learning" or "pattern recognition and machine learning", basically "unless you took two semesters of information theory, go back to uni"- and "how to install python and pandas"This one is neither of them. The book starts immediately - it does not waste time, but has enough details so you know which symbol is what. And it does not "slap you" with too much mathematical noodles as if it was saying "unless you're 24, it's too late for you", there are there, but commented enough to get them without opening an encyclopedia.Now it's my favorite book on the subject, but I admit I am still in first 10%. I just really like the style.about me: ex-Mozilla, ex-Google, currently NVidia, finished uni 10 years ago.

super fast book delivery and it was in pristine condition, thx

Probably the best book on the subject! Not suited to beginners, BUT this was the first book I read on the subject and although difficult I was able to work through it with the help of Google. Things quickly started to click into place. A must have for anybody seriously interested in NN's and AI

Best book for understanding the fundamentals of DL.