Scientific Machine Learning: Optimal Errors

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Neural Networks: Perceptron and Backpropagation

Covers the basics of neural networks, including the perceptron model and backpropagation.

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Scaling Language Models: Efficiency and Deployment

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Machine Learning Theory: High-Dimensional Perspectives

Explores high-dimensional perspectives in machine learning theory, shedding light on new insights and challenges.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Feature Selection, Kernel Regression, Neural Networks Playground

Covers feature selection, kernel regression, and neural networks through exercises.

Phase Transitions: Understanding Thermodynamic Stability

Explores phase transitions in thermodynamics, focusing on stability criteria and real-world applications.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.