Ebook Free , by Charu C. Aggarwal
January 30, 2013
Phiafield
Ebook Free , by Charu C. Aggarwal
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, by Charu C. Aggarwal
Ebook Free , by Charu C. Aggarwal
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Product details
File Size: 29883 KB
Print Length: 497 pages
Publisher: Springer; 1st ed. 2018 edition (August 25, 2018)
Publication Date: August 25, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07FKF5HY7
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Amazon Best Sellers Rank:
#167,484 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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.
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.
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.
The "Neural Networks and Deep Learning" book is an excellent work.The material which is rather difficult, is explained well and becomes understandable(even to a not clever reader, concerning me!).The overall quality of the book is at the level of the other classical "Deep Learning" bookof Goodfellow, both books are outstanding and can help to providetheir own views at the exploding domain of deep learning.In my opinion the reader can better benefit from studying both books together,they provide complementary insights to the subtle and intricate mathematics andalgorithms.
The book fires off statement after statement with very little context. References and comparisons are made to algorithims and concepts that haven't even been introduced yet. If I hadn't already read introductions to neural networks online, I'd have no idea what he's talking about, and seldom do when he splatter shots a new concept onto the page. In which case I'll stick with the online references.
The book is awesome. The theory in the book gives great intuition on what is going on with deep neural networks. With average knowledge of calculus and linear algebra, the math of deep learning is easily understood in the book with the great intuition the author builds prior to the mathematical equations. The book allowed me to build deep learning applications from scratch and makes it almost trivial to build them with the available libraries out there like keras, tensorflow, or pytorch.
An Excellent book for students, researchers and engineers at all levels.This book tells you the most important background, theories, models, methods and practices in deep learning. Unlike many serious books that are hard to understand, it is really enjoyable to read this book that clearly explains how the whole stuff works together from the very begining to the very end in concise and most accurate language.I have been working in this area for more than one decade from the era when we simply called "deep learning" "neural networks," and well followed it evolving into deep learning and AI. But I still learn a lot and refresh my idea when I read many chapters in this book.Definitly will recommend it to all my friends and colleagues.
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