Ebook: Fundamentals of Deep Learning
Author: Nithin Buduma, Nikhil Buduma, Joe Papa
- Year: 2022
- Publisher: O'Reilly Media
- Edition: 2
- Language: English
- epub
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.
• 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
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.
• 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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