Advanced Optimization in Machine Learning

This tutorial series balances theoretical foundations (~30 minutes) with practical implementations. It includes algorithm explanations, hands-on implementations, comparative experiments, and deployment-aware optimization techniques.

⚙️ The tutorial is divided into four parts: foundational theory, core method implementation, comparative experiments, and deployment-oriented optimization.

1. Part 1 - Optimization Techniques in Machine Learning

Focus: Theoretical overview of classical and modern optimization algorithms.

• Why Optimization Matters in ML
• Gradient-Based Optimization
• Second-Order & Advanced Methods
• Hyperparameter Optimization

2. Part 2 - Practical Implementation of Optimization Methods

Focus: From-scratch implementation using NumPy.

• Simple NumPy Neural Network
• Activation Functions: ReLU, Tanh
• Gradient Descent Variants
• Linear Quantization
• Single-line TensorFlow Comparison

3. Part 3 - Advanced Optimization ML

Focus: Comparative optimizer performance, learning rate scheduling, and regularization.

• Optimizer dynamics (SGD, Adam, RMSProp)
• Performance comparison using Keras
• Learning Rate Scheduling: ExponentialDecay, CosineDecay, OneCycleLR
• Dropout & L2 Regularization
• Hyperparameter tuning with Optuna
• Guided optimization challenge

4. Part 4 - Neural Architecture Search & Deployment Optimization

Focus: Optimization beyond training—design, compression, and deployment.

• Introduction & Dataset Overview
• Neural Architecture Search (NAS)
• Post-Training Quantization
• Quantization-Aware Training (QAT)

5. requirements.txt

Install required packages:

pip install -r requirements.txt