Deep Learning from first principles: Second Edition: In vectorized Python, R and OctaveКНИГИ » ПРОГРАММИНГ
Название: Deep Learning from first principles: Second Edition: In vectorized Python, R and Octave Автор: Tinniam V. Ganesh Издательство: Amazon Digital Services LLC ASIN: B07LBG542L Год: 2018 Страниц: 770 Язык: английский Формат: epub, pdf (conv) Размер: 10.4 MB
The last decade and some, has witnessed some remarkable advancements in the area of Deep Learning. This area of Artificial intelligence (AI) has proliferated into many branches - Deep Belief Networks, Recurrent Neural Networks, Convolution Neural Networks, Adversorial Networks, Reinforcement Learning, Capsule Networks and the list goes on. These years have also resulted in Deep Learning to move from the research labs and closer to the home, thanks to progress in hardware, strorage and cloud technology.
This is the second edition of the book. The code has been formatted with fixed with a fixed width font, and includes line numbering. This book derives and builds a multi-layer, multi-unit Deep Learning from the basics. The first chapter starts with the derivation and implementation of Logistic Regression as a Neural Network. This followed by building a generic L-Layer Deep Learning Network which performs binary classification. This Deep Learning network is then enhanced to handle multi-class classification along with the necessary derivations for the Jacobian of softmax and cross-entropy loss. Further chapters include different initialization types, regularization methods (L2, dropout) followed by gradient descent optimization techniques like Momentum, Rmsprop and Adam. Finally the technique of gradient checking is elaborated and implemented. All the chapters include implementations in vectorized Python, R and Octave. Detailed derivations are included for each critical enhancement to the Deep Learning. By the time you reach the last chapter, the implementation includes fully functional L-Layer Deep Learning with all the bells and whistles in vectorized Python, R and Octave. The code, for all the chapters, has been included in the Appendix section
Preface Introduction 1.Logistic Regression as a Neural Network 2.Implementing a simple Neural Network 3.Building a L- Layer Deep Learning Network 4.Deep Learning network with the Softmax 5.MNIST classification with Softmax 6.Initialization, regularization in Deep Learning 7.Gradient Descent Optimization techniques 8.Gradient Check in Deep Learning 1.Appendix A 2.Appendix 1 – Logistic Regression as a Neural Network 3.Appendix 2 - Implementing a simple Neural Network 4.Appendix 3 - Building a L- Layer Deep Learning Network 5.Appendix 4 - Deep Learning network with the Softmax 6.Appendix 5 - MNIST classification with Softmax 7.Appendix 6 - Initialization, regularization in Deep Learning 8.Appendix 7 - Gradient Descent Optimization techniques 9.Appendix 8 – Gradient Check References
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From Machine Learning To Deep Learning Название: From Machine Learning To Deep Learning Автор: Khaled Bayoudh Издательство: Khaled Bayoudh Год: 2017 Страниц: 166 Язык: английский...
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Deep Learning from first principles: Second Edition: In vectorized Python, R and Octave 
