Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning | SpringerLinkIt's important to keep up with indusctry - subscribe! If you have an interesting and valuable suggestion we could have missed, please let us know in the comments below. Deep Learning is, perhaps, the only Bible of its kind written on artificial intelligence and machine learning, deep learning included. This is a mandatory read for students and academics, hence — be prepared for a highly technical and vastly academic language. The book is both available for free on the website and for a price on Amazon. There are also multiple resources available on the site, including lectures and exercises that go along with the book. It provides [a] much-needed broad perspective and mathematical preliminaries for software engineers and students entering the field, and serves as a reference for authorities.
Deep Learning Chapter 1 Introduction presented by Ian Goodfellow
Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning
I'm looking for foundational textbooks, manuals, or even long articles deep learning applications. The book talks about the quest of researchers to build a truly artificial general intelligence. While the second part of the book is less interesting, the idea conveyed in the book definitely get hyped up about the promises on AI. As a voracious reader, I enjoy reading both technical and non-technical books. My 2c on a few good books:.
On the exercises and problems. Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning. Backpropagation: the big picture. Improving the way neural networks learn The cross-entropy cost function Overfitting and regularization Weight initialization Handwriting recognition revisited: the code How to choose a neural network's hyper-parameters? Other techniques.
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Genetic Programming and Evolvable Machines. Deep Learning provides a truly comprehensive look at the state of the art in deep learning and some developing areas of research. The authors are Ian Goodfellow, along with his Ph. All three are widely published experts in the field of artificial intelligence AI. In addition to being available in both hard cover and Kindle the authors also make the individual chapter PDFs available for free on the Internet. A non-mathematical reader will find this book difficult. A comprehensive, well cited coverage of the field makes this book a valuable reference for any researcher.