FUNDAMENTALS OF DEEP LEARNING AND COMPUTER VISION

By:

SINGH NIKHIL

Contributor(s):

PARAS AHUJA

Material type:

TextPublication details: NEW DELHI BPB PUBLICATIONS INDIA 2020Edition: FIRST EDITIONDescription: X 184PISBN:

978*-93-8851-185-8

Other title:

A Complete Guide to become an Expert in Deep Learning and Computer Vision

Subject(s):

Summary: "Fundamentals of Deep Learning and Computer Vision" by Nikhil Singh is a comprehensive guide designed to help readers become experts in deep learning and computer vision. The book starts with setting up a Python virtual environment and introduces the TensorFlow deep learning framework. It covers fundamental concepts such as neural networks, loss functions, gradient descent optimization, activation functions, and backpropagation for training multilayer perceptrons.

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Holdings
Cover image	Item type	Current library	Home library	Collection	Shelving location	Call number	Materials specified	Vol info	URL	Copy number	Status	Notes	Date due	Barcode	Item holds	Item hold queue priority	Course reserves
	Reference	Central Library, Aditya Institute of Technology and Management GF-04/L	Central Library, Aditya Institute of Technology and Management	Computer Science		006.37 SIN (Browse shelf(Opens below))					Not for Loan	DEEP LEARNING		54087

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Previous	006.3 SUT REINFORCEMENT LEARNING AN INTRODUCTION	006.31 DHA MACHINE LEARNING ESSENTIALS YOU ALWAYS WANTED TO KNOW	006.31 DHA MACHINE LEARNING ESSENTIALS YOU ALWAYS WANTED TO KNOW	006.37 SIN FUNDAMENTALS OF DEEP LEARNING AND COMPUTER VISION	055.133 RAH PYTHON DATA VISUALIZATION ESSENTILAS GUIDE	055.133 RAH PYTHON DATA VISUALIZATION ESSENTILAS GUIDE	616.075 ZHO DEEP LEARNING FOR MEDICAL IMAGE ANALYSIS	Next

Deep Learning

"Fundamentals of Deep Learning and Computer Vision" by Nikhil Singh is a comprehensive guide designed to help readers become experts in deep learning and computer vision. The book starts with setting up a Python virtual environment and introduces the TensorFlow deep learning framework. It covers fundamental concepts such as neural networks, loss functions, gradient descent optimization, activation functions, and backpropagation for training multilayer perceptrons.

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