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{ "item_title" : "Introduction to Deep Learning", "item_author" : [" Lakshmi Prasad Mudarakola", "Padmaja M "], "item_description" : "The Deep Learning textbook provides a comprehensive introduction to the field of deep learning-a branch of machine learning focused on algorithms inspired by the structure and function of the brain's neural networks. It covers foundational topics such as linear algebra, probability, and information theory before delving into core deep learning concepts, including neural networks, optimization techniques, and regularization.Advanced topics include convolutional networks, sequence modeling (such as RNNs), generative models, and deep reinforcement learning. The book also explores theoretical foundations and future directions in deep learning research.Written by leading experts, this textbook is widely used in both academia and industry, offering practical insights alongside deep theoretical knowledge. It's ideal for students, researchers, and professionals looking to understand and apply deep learning techniques.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/6/20/843/823/6208438233_b.jpg", "price_data" : { "retail_price" : "50.00", "online_price" : "50.00", "our_price" : "50.00", "club_price" : "50.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Introduction to Deep Learning|Lakshmi Prasad Mudarakola

Introduction to Deep Learning : Theory and Practice

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Overview

The Deep Learning textbook provides a comprehensive introduction to the field of deep learning-a branch of machine learning focused on algorithms inspired by the structure and function of the brain's neural networks. It covers foundational topics such as linear algebra, probability, and information theory before delving into core deep learning concepts, including neural networks, optimization techniques, and regularization.Advanced topics include convolutional networks, sequence modeling (such as RNNs), generative models, and deep reinforcement learning. The book also explores theoretical foundations and future directions in deep learning research.Written by leading experts, this textbook is widely used in both academia and industry, offering practical insights alongside deep theoretical knowledge. It's ideal for students, researchers, and professionals looking to understand and apply deep learning techniques.

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Details

  • ISBN-13: 9786208438234
  • ISBN-10: 6208438233
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: April 2025
  • Dimensions: 9 x 6 x 0.18 inches
  • Shipping Weight: 0.25 pounds
  • Page Count: 76

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