Deep learning from scratch: building with Python from first principles
Weidman, Seth
Deep learning from scratch: building with Python from first principles - Sebastopol O'Reilly Media 2019 - xiv, 235 p.
Description
All Indian Reprints of O'Reilly are printed in Grayscale
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects.
This book provides:
Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch framework
9789352139026
Machine learning
Python (Computer program language)
Neural networks (Computer science)
Artificial intelligence
006.32 / WEI
Deep learning from scratch: building with Python from first principles - Sebastopol O'Reilly Media 2019 - xiv, 235 p.
Description
All Indian Reprints of O'Reilly are printed in Grayscale
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects.
This book provides:
Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanationsfor understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch framework
9789352139026
Machine learning
Python (Computer program language)
Neural networks (Computer science)
Artificial intelligence
006.32 / WEI