Principal Component Analysis and Singular Value Decomposition - Spring 2023
Master dimensionality reduction techniques through PCA and SVD, exploring multicollinearity, vector basis, and practical applications with hands-on demonstrations for effective data analysis.
Parameter-Efficient Fine-Tuning with LoRA - Optimizing LLMs for Local GPU Training
Master PEFT LoRA techniques to efficiently fine-tune large language models on local GPUs through low-rank adaptation, reducing memory requirements while maintaining model performance.
Linear Models and Their Geometry in Machine Learning - Lecture 6
Explore the geometry and fundamental concepts of linear classifiers, examining their role as essential tools in machine learning classification problems.
Faster Matroid Intersection - Winter 2020 Theory Seminar
Explores faster algorithms for matroid intersection, improving exact and approximate solutions using independence and rank oracles. Advances in runtime complexity for this classic problem in combinatorial optimization.
Functional Collection Programming with Semi-Ring Dictionaries
Explore semi-ring dictionaries, a versatile collection type for functional programming. Learn about SDQL language and its optimizations for efficient database and linear algebra processing.
Code-It-Yourself 3D Graphics Engine - Triangles and Projection - Part 1
Learn to build a 3D graphics engine from scratch, covering triangles, projection matrices, and basic rotation. Gain hands-on experience in creating a rotating cube using fundamental 3D graphics concepts.
AlphaTensor by DeepMind Explained
Explore DeepMind's AlphaTensor, a revolutionary AI system that discovers efficient matrix multiplication algorithms, outperforming human-designed methods and advancing computational efficiency across various fields.
ChatGPT Teaches Numpy - Python Numpy Tutorial
Comprehensive tutorial on NumPy: from basics to advanced concepts. Learn array creation, manipulation, mathematical operations, reshaping, and integration with Python data structures.
Continuous Algorithms - Sampling and Optimization in High Dimension
Explore high-dimensional sampling and optimization techniques, including cutting plane methods, interior-point algorithms, and Riemannian Hamiltonian Monte Carlo, with insights on complexity and open problems.
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.
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.
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.
Subspace - A GUI Front End for the Wireguard VPN Server
Learn to install and configure Subspace, a free open-source GUI for Wireguard VPN servers. Covers manual setup on Ubuntu with Docker and includes an automated installation script option.
Ergodicity in Smooth Dynamics 1
Explore ergodicity in smooth dynamics, covering introductory concepts, examples, proofs, and higher dimensions with experts Jana Rodriguez-Hertz and Amie Wilkinson.
ABY3 - A Mixed Protocol Framework for Machine Learning
Explore privacy-preserving machine learning with ABY3, a framework addressing data collection concerns through encrypted computations and secure multi-party protocols.
Secure Outsourced Matrix Computation and Application to Neural Networks
Explore practical solutions for encrypted matrix operations in homomorphic encryption, including novel encoding methods and efficient evaluation strategies for basic matrix arithmetic and neural networks.
Linearity Testing Over the Biased Cube
Explores linearity testing in p-biased settings, presenting a 4-query test with optimal soundness. Discusses structural results and direct product tests for functions correlated to linear functions under random restrictions.
Fine-Grained Complexity - Lecture 1
Explore cutting-edge research in fine-grained complexity theory with MIT expert Virginia Vassilevska Williams, delving into advanced algorithmic concepts and their applications.
Machine Learning for Signal Processing - Data Compression and Denoising
Explore machine learning for signal processing, focusing on data compression and noise removal using PCA. Learn linear algebra applications and alternative techniques for effective data analysis.
Preparing Data for LLMs and Generative AI Workflows
Explore data preparation essentials for LLMs and Gen-AI, covering Lakehouse AI, vector databases, model challenges, and building custom solutions with ML Flow AI Gateway.
Dimensionality Reduction for Matrix- and Tensor-Coded Data
Explore matrix and tensor factorizations for data compression, including PCA, NMF, and CP tensor decomposition. Learn theoretical foundations and practical applications in scientific domains.