Verma P Handbook of Deep Learning Models Vol One Fundamentals 2026 | 9.46 MB

Title: Handbook of Deep Learning Models
Author: Verma, Parag;Sankar, Er. Devarasetty Purna;Bhardwaj, Anuj;Chaudhari, Vaibhav;Pandey, Arnav;Dumka, Ankur; & Er. Devarasetty Purna Sankar & Anuj Bhardwaj & Vaibhav Chaudhari & Arnav Pandey & Ankur Dumka

Description:
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating self-driving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a PhD in machine learning and mathematics.
The updated second edition of this book describes the intuition behind these innovations without jargon or complexity. Python-proficient programmers, software engineering professionals, and computer science majors will be able to reimplement these breakthroughs on their own and reason about them with a level of sophistication that rivals some of the best developers in the field.

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• Learn the mathematics behind machine learning jargon
  
• Examine the foundations of machine learning and neural networks
  
• Manage problems that arise as you begin to make networks deeper
  
• Build neural networks that analyze complex images
  
• Perform effective dimensionality reduction using autoencoders
  
• Dive deep into sequence analysis to examine language
  
• Explore methods in interpreting complex machine learning models
  
• Gain theoretical and practical knowledge on generative modeling
  
• Understand the fundamentals of reinforcement learning
  

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