DEEP LEARNING FROM SCRATCH: BUILDING WITH PYTHON FROM FIRST PRINCIPLES

By:

WEIDMAN, SETH

Material type: Text

TextPublication details: Navi Mumbai Shroff Publishers and Distributors Pvt. Ltd 2019Description: xiv, 235pISBN:

9789352139026

Subject(s):

NEURAL NETWORKS (COMPUTER SCIENCE), MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, PYTHON (COMPUTER PROGRAM LANGUAGE)

DDC classification:

006.32/WEI

Summary: This book provides: 1) Extremely clear and thorough mental models accompanied by working code examples and mathematical explanations for understanding neural networks 2) Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework 3) Working implementations and clear-cut explanations of convolutional and recurrent neural networks 4) Implementation of these neural network concepts using the popular PyTorch framework

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Holdings
Item type	Current library	Call number	Status	Notes	Date due	Barcode
Books	Symbiosis Institute of Computer Studies and Research Python	006.32/WEI (Browse shelf(Opens below))	Available	NEURAL NETWORKS (COMPUTER SCIENCE), MACHINE LEARNING, ARTIFICIAL INTELLIGENCE, PYTHON (COMPUTER PROGRAM LANGUAGE)		SICSR-B-19611

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Previous	006.3/MAN Deep Learning with Applications Using Python	006.31/MIS Practical Explainable AI Using Python: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks	006.312/MCK PYTHON FOR DATA ANALYSIS	006.32/WEI DEEP LEARNING FROM SCRATCH: BUILDING WITH PYTHON FROM FIRST PRINCIPLES	006.35/BEY APPLIED NATURAL LANGUAGE PROCESSING WITH PYTHON	006.7/DEY HANDS-ON IMAGE PROCESSING WITH PYTHON	006.7/DEY HANDS-ON IMAGE PROCESSING WITH PYTHON	Next

This book provides: 1) Extremely clear and thorough mental models accompanied by working code examples and mathematical explanations for understanding neural networks 2) Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework 3) Working implementations and clear-cut explanations of convolutional and recurrent neural networks 4) Implementation of these neural network concepts using the popular PyTorch framework

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