Graphon Cross-Validation: Assessing Models on Network Data
Explore graphon cross-validation techniques for network data analysis, enhancing model accuracy and generalizability through innovative validation methods and theoretical insights.
Beating the Odds - Learning or Hallucinating? The Science of Data Today
Explore the intersection of data science, learning, and hallucination with insights from a renowned mathematician on current trends and challenges in the field.
Prepping Data for Analysis Using R
Learn techniques for preparing data for analysis using R, covering common issues, automated solutions, and practical demonstrations for handling missing values and categorical variables.
Introduction to Lasso and Ridge Regression Models in R
Master linear regression regularization techniques through Lasso and Ridge methods in R, focusing on preventing overfitting and improving model reliability for more accurate predictions and estimations.
Machine Learning Mid-Semester Review - Lecture 15
Dive into a comprehensive mid-semester review of fundamental machine learning concepts, covering key principles and techniques explored throughout the academic term.
Understanding Grokking - A New Performance Phase in Large Language Models
Discover how extended training of LLMs leads to "grokking" - a breakthrough phase where models develop superior generalization abilities and form geometric patterns in embedding spaces.
Machine Learning Fundamentals - Lecture 4
Dive into advanced machine learning concepts and algorithms through comprehensive coverage of key theoretical foundations and practical implementations in data science.
Machine Learning Fundamentals - Lecture 2
Dive into machine learning fundamentals with a comprehensive exploration of decision trees, data classification techniques, and algorithmic approaches to pattern recognition.
Machine Learning Fundamentals - Lecture 7
Dive into advanced machine learning concepts and algorithms through comprehensive coverage of key theoretical foundations and practical implementations in data science.
Machine Learning Fundamentals - Lecture 5
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Decision Trees in Machine Learning: Practical Concerns and Overfitting - Lecture 5
Delve into practical aspects of decision tree learning, exploring implementation challenges and understanding how to address overfitting in machine learning models.
Decision Trees and Overfitting in Machine Learning - Lecture 6
Dive into advanced concepts of decision trees, focusing on overfitting challenges and practical solutions for building more robust machine learning models.
Reconsidering Overfitting in the Age of Overparameterized Models
Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.
Building Makemore - MLP
Implement a multilayer perceptron character-level language model, covering machine learning basics like model training, hyperparameter tuning, evaluation, and addressing under/overfitting.
Regularization Part 1 - Ridge (L2) Regression
Learn Ridge Regression to prevent overfitting, solve unsolvable equations, and optimize models. Covers main ideas, details, applications for various data types and models, and usage with limited data.
Neural Nets for NLP - Debugging Neural Nets for NLP
Practical strategies for debugging and optimizing neural networks in NLP, covering issues like training, overfitting, initialization, and evaluation metrics.
Debugging Neural Nets for NLP
Comprehensive guide to diagnosing and resolving issues in neural networks for NLP, covering training, decoding, overfitting, and optimization techniques.
Neural Nets for NLP - Debugging Neural Nets
Learn techniques for identifying and resolving issues in neural networks for NLP, covering training and test-time problems, optimization strategies, and performance analysis.
CMU Advanced NLP: Text Classification
Comprehensive introduction to neural networks for NLP, covering core concepts like forward/backward algorithms, parameter updates, and training techniques for text classification tasks.
Machine Learning Explained - Teaching a Robot to Walk Tutorial
Comprehensive tutorial on machine learning concepts, from handwriting recognition to reinforcement learning, with practical demonstrations using neural networks and robot simulations.
Overfitting, Underfitting, and Random Forests in Machine Learning - Day 10
Explore overfitting, underfitting, and random forests in machine learning. Learn to build accurate models and apply these concepts to improve your ML skills.