Deep Learning Theory

 This book is a perfect example of why, in some areas of science, Ph.D.-level studies are indispensable. If we review this book and compare it to text from 10-20 years ago, it will be evident that the complexity has increased. We need to have enough time for studying, programming, and inventing new algorithms. A master's degree program might not be enough time to get started in such areas as machine learning. 

Developing new deep learning neural network algorithms is complex. It requires a solid understanding of calculus, linear algebra, probabilistic theory, and software engineering. This book focuses primarily on deep #perceptron theory and is an excellent advanced resource for a relatively small group of developers and scientists. Hopefully, texts like this will help grow the deep learning R&D community. 

The authors approach the substantial complexity of deep learning by decomposing the theories into elementary constituents. As a result, it is easier to analyze phenomena that emerge from the vast number of system components that comprise typical deep neural networks. #deeplearning #neuralnetworks #linearalgebra #calculus #probabilistictheory #ai #artificialintelligence #softwareengineering