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Bsc agriculture entrance exam

Bsc agriculture entrance exam

₹200 ₹450
56% off

Mass Communication is the process of exchanging information through the large segments of the people, in other words it is the mode of imparting and exchanging the information to the wide range of people. It is one of the most creative carriers which attract lot of young minds these days. Mass Communication and Journalism mostly covers 3 areas i.e. Advertisements, Media and Public Relations. The revised edition of ‘The Ultimate Guide for Journalism & Mass Communication” has been comprehensively designed that covers the syllabi of almost all the Mass Media Institutions/ Universities. The book is divided into 4 sections and each section is further divided into chapters with complete theory that has been synchronized with the syllabus. This book provides Section wise Practice i.e. each section is carried with 2 Section Tests in order to clear all the queries regarding the subjects simultaneously. In order to make candidates acquainted with latest pattern of the examination ample amount of MCQs have been provided in the book moreover, for complete practice Previous Years’ Solved Papers [2019-2015] and 3 Practice Sets have been provided in this book. Each question provided in the book is well explained in a lucid language which makes candidates to memorize the concepts easily and quickly. This book is highly useful for Bachelor of Journalism in GGSIPU and other (IIMC, MICA) prestigious Universities Entrance Examination. Thorough preparation done from this book will ensure the selection of the candidates in a good colleges TABLE OF CONTENT Solved Paper 2019 – 2015, Mass Communication/Media Aptitude, General Awareness, English Language/Comprehension, Reasoning & Logical Deduction, Crack Sets (1-3), Answers to Section Tests & Crack Sets.

2 years ago
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Deep Learning By Iam Goodfellow,Yoshua Bengio and Aaron Courville

Deep Learning By Iam Goodfellow,Yoshua Bengio and Aaron Courville

₹1000 ₹6246
84% off

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

2 years ago
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