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.
Principal Component Analysis - PCA, Step-by-Step
Step-by-step guide to Principal Component Analysis (PCA), explaining its importance in data analysis and machine learning. Covers 2D and 3D data examples, PC calculation, and interpretation of results.
Principal Component Analysis - PCA Clearly Explained - 2015
Clear explanation of Principal Component Analysis, covering dimensions, variance, loading scores, and key concepts for interpreting PCA plots in RNA-seq data analysis.
Neural Nets for NLP 2018 - Learning from-for Knowledge Graphs
Explore neural network techniques for knowledge graph construction, relation extraction, and embedding refinement in natural language processing.
Neural Nets for NLP - Latent Variable Models
Explore latent variable models in NLP, covering variational autoencoders, discrete variables, and practical applications. Gain insights into generative modeling and advanced neural network techniques.
CMU Advanced NLP 2022 - Latent Variable Models
Explore advanced NLP concepts: latent variables, variational autoencoders, and their applications in natural language processing. Gain insights into generative models and feature learning.
CMU Advanced NLP: Structured Learning Algorithms
Explore advanced NLP techniques like reinforcement learning, structured perceptron, and exposure bias remedies. Gain insights into globally normalized models and structured prediction algorithms.
CMU Advanced NLP 2021 - Latent Variable Models
Explore advanced NLP concepts like variational autoencoders, discrete latent variables, and their applications in natural language processing tasks.
TU Wien Rendering - Metropolis Light Transport
Explore Metropolis Light Transport, an advanced rendering technique that outperforms Bidirectional Path Tracing in difficult scenes, offering optimal importance sampling and improved convergence for caustics.
Rendering - Path Tracing Implementation & Code Walkthrough
Comprehensive walkthrough of implementing a path tracer in C++, covering key concepts like vector class, object representation, camera setup, sampling, and rendering techniques.
TU Wien Rendering - Reinhard's Tone Mapper
Explore Reinhard's tone mapping algorithm for converting radiance to RGB in rendering, covering both local and global versions used in major renderers.
Rendering - Tone Mapping Basics
Explore tone mapping techniques for converting radiance to RGB in rendering, covering dynamic range, naive and effective mappers, and common misuses.
Forecast Any Mathematical Number Sequence with Regression - Machine Learning
Learn to generate and forecast mathematical number sequences using Python, pandas, numpy, and scikit-learn. Visualize data with matplotlib and apply regression models to predict future numbers in various function-based series.
Everything You Need to Know About Tensors in Deep Learning With PyTorch
Comprehensive guide to PyTorch tensors: creation, operations, data types, and GPU usage. Hands-on examples cover essential concepts for deep learning beginners.
Graph Neural Networks Implementation in Python
Learn Graph Neural Networks implementation in Python, covering graph representations, node embeddings, message passing, and practical applications using NetworkX and PyG libraries.
World's Hardest Jigsaw vs. Puzzle Machine - All White
Watch a puzzle machine tackle the world's hardest jigsaw—an all-white puzzle. Explore image processing, mathematical algorithms, and innovative engineering solutions in this challenging project.
Methods for L_p-L_q Minimization in Image Restoration and Regression - SIAM-IS Seminar
Explore methods for image restoration and regression using $\ell_p$-$\ell_q$ minimization, covering problem formulation, solution techniques, and applications in this advanced mathematical seminar.
Optimization, Complexity and Math - Can We Prove P!=NP by Gradient Descent?
Explore optimization, complexity, and mathematics with Avi Wigderson. Delve into perfect matchings, symbolic matrices, and operator scaling, connecting diverse areas of mathematics and computer science.
DeepSet and Derivative Networks for Solving Symmetric Problems
Explore DeepSet networks for solving symmetric problems in high-dimensional PDEs and control, with applications in finance and mean-field control. Learn about their advantages and performance in various scenarios.
Vectorization, Decomposition, and PDE Identification in Imaging and Inverse Problems
Explore image vectorization, decomposition, and PDE identification techniques. Learn about silhouette extraction, image separation, and differential equation recovery from discrete data.
Coding Theoretic Framework for Private Information Retrieval - SIAM SAGA Seminar
Explore private information retrieval in coded storage systems, focusing on algebraic-geometric codes and star product schemes for adjustable storage overhead and server collusion protection.