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Statistics
Machine Learning
1. Introduction to Machine Learning
2. Mathematical and Statistical Foundations
3. Data Preprocessing and Feature Engineering
4. Supervised Learning
5. Unsupervised Learning
6. Model Evaluation and Validation
7. Ensemble Methods and Advanced Techniques
8. Deep Learning and Neural Networks
9. Reinforcement Learning
10. Advanced Topics and Specialized Areas
11. Machine Learning Operations and Deployment
Deep Learning and Neural Networks
Neural Network Fundamentals
Biological Inspiration
Neurons and Synapses
Action Potentials
Learning in Biological Systems
Artificial Neurons
Mathematical Model
Weighted Inputs
Bias Terms
Activation Functions
The Perceptron
Linear Classifier
Perceptron Learning Rule
Limitations
XOR Problem
Multi-Layer Perceptrons
Hidden Layers
Universal Approximation Theorem
Network Architecture
Depth vs. Width
Activation Functions
Linear Activation
Identity Function
Limitations
Sigmoid Functions
Logistic Sigmoid
Mathematical Properties
Vanishing Gradient Problem
Output Range
Hyperbolic Tangent
Mathematical Definition
Zero-Centered Output
Comparison with Sigmoid
Rectified Linear Unit
ReLU Definition
Advantages
Dead Neuron Problem
ReLU Variants
Leaky ReLU
Parametric ReLU
Exponential Linear Unit
Swish
GELU
Softmax Function
Probability Distribution
Multi-Class Classification
Temperature Parameter
Forward Propagation
Layer-by-Layer Computation
Matrix Operations
Vectorization
Computational Graphs
Training Neural Networks
Loss Functions
Regression Loss Functions
Mean Squared Error
Mean Absolute Error
Huber Loss
Classification Loss Functions
Binary Cross-Entropy
Categorical Cross-Entropy
Sparse Categorical Cross-Entropy
Hinge Loss
Focal Loss
Backpropagation Algorithm
Chain Rule Application
Gradient Computation
Error Propagation
Weight Update Rules
Computational Efficiency
Optimization Algorithms
Gradient Descent Variants
Batch Gradient Descent
Stochastic Gradient Descent
Mini-Batch Gradient Descent
Momentum Methods
Classical Momentum
Nesterov Accelerated Gradient
Adaptive Learning Rate Methods
AdaGrad
RMSprop
Adam
AdamW
Nadam
Learning Rate Scheduling
Step Decay
Exponential Decay
Cosine Annealing
Warm Restarts
Regularization Techniques
Weight Decay
L2 Regularization
Implementation Details
Dropout
Random Neuron Deactivation
Training vs. Inference
Dropout Variants
Batch Normalization
Internal Covariate Shift
Normalization Process
Learnable Parameters
Benefits
Layer Normalization
Group Normalization
Early Stopping
Validation Monitoring
Patience and Restoration
Convolutional Neural Networks
Convolution Operation
Mathematical Definition
Feature Maps
Kernel/Filter Concepts
Local Connectivity
Convolutional Layers
Multiple Filters
Stride Parameter
Padding
Valid Padding
Same Padding
Dilation
Pooling Layers
Max Pooling
Average Pooling
Global Pooling
Stride and Window Size
CNN Architectures
LeNet
AlexNet
VGGNet
ResNet
Inception Networks
DenseNet
EfficientNet
Applications
Image Classification
Object Detection
Semantic Segmentation
Style Transfer
Recurrent Neural Networks
Sequential Data Processing
Hidden State Concept
Vanilla RNN
Architecture
Forward Pass
Backpropagation Through Time
Vanishing and Exploding Gradients
Problem Description
Gradient Clipping
Architectural Solutions
Long Short-Term Memory
Cell State
Forget Gate
Input Gate
Output Gate
LSTM Variants
Gated Recurrent Units
Reset Gate
Update Gate
Simplified Architecture
Bidirectional RNNs
Forward and Backward Processing
Context from Both Directions
Applications
Language Modeling
Machine Translation
Time Series Prediction
Speech Recognition
Advanced Architectures
Autoencoders
Encoder-Decoder Structure
Dimensionality Reduction
Denoising Autoencoders
Sparse Autoencoders
Variational Autoencoders
Attention Mechanisms
Attention Concept
Attention Weights
Soft vs. Hard Attention
Self-Attention
Transformer Architecture
Multi-Head Attention
Positional Encoding
Encoder-Decoder Structure
Layer Normalization
Feed-Forward Networks
Generative Adversarial Networks
Generator Network
Discriminator Network
Adversarial Training
Nash Equilibrium
Training Challenges
GAN Variants
Transfer Learning
Pretrained Models
ImageNet Pretrained Models
Language Model Pretraining
Domain Adaptation
Fine-Tuning Strategies
Feature Extraction
Full Fine-Tuning
Layer-Wise Learning Rates
Gradual Unfreezing
Domain Adaptation
Source and Target Domains
Distribution Shift
Adaptation Techniques
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7. Ensemble Methods and Advanced Techniques
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9. Reinforcement Learning