Ebook: Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
- Genre: Computers // Cybernetics: Artificial Intelligence
- Tags: Machine Learning, Neural Networks, Deep Learning, Reinforcement Learning, Regression, Python, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Principal Component Analysis, TensorFlow, Gradient Descent, Logistic Regression, Long Short-Term Memory, Overfitting, Turing Machine, word2vec, Bellman Equation, Markov Models, Backpropagation
- Year: 2022
- Publisher: O'Reilly Media
- City: Sebastopol, CA
- Edition: 2
- Language: English
- pdf
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