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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
Single-Layer Neural Networks
Linear Regression Model
Linear Neuron
Single-Layer Perceptron
Softmax Regression
Cross-Entropy Loss Function
Multi-Layer Neural Networks
Universal Approximation Theorem
Feature Hierarchy
Vanishing Gradient Problem
ReLU Activation Function
Multi-Layer Fully Connected Neural Network
The Learning Process of Neural Networks
Initialization
Forward Propagation
Loss Computation
Backpropagation
Parameter Update
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