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Deep Learning
Fundamentals & Summary
Deep Learning Landscape
Introduction to Deep Learning
Feedforward Neural Networks
Probability & Statistics in DL
Loss Functions
Convolutional Neural Networks
Recurrent Neural Networks
Generative Models
Graph Neural Networks
Large Language Models
Foundation Models
New Sequence Model Paradigms
Optimization & Regularization
DL Frontiers
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Table of Contents
Probability as a Tool
Modeling Ignorance
Random Variables
Expectation
Distribution
Model Parameters
Frequency and Limit Theorems
Independent and Identically Distributed Samples
Frequency
Empirical Distribution
Sample Mean
Law of Large Numbers
Central Limit Theorem
Statistics
Statistical Inference and Parameter Estimation
Likelihood
Likelihood Function
Maximum Likelihood Estimation
Information Theory
Maximum Entropy
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