CUTLASS: A CUDA C++ Template Library for Accelerating Deep Learning Computations
Explore CUTLASS, an open-source CUDA C++ template library for optimizing deep learning computations on NVIDIA GPUs. Learn to develop high-performance custom kernels using Tensor Core programming.
Two Dimensional Arrays - C Programming
Learn to work with 2D arrays in C, including initialization, subscripts, and printing. Master tabular data representation for applications like student marks and matrix operations.
Introduction to Text Analytics with R - Overview
Explore text analytics with R: tokenization, feature engineering, classification models. Learn data cleaning, analysis, and visualization using a spam dataset. Practical introduction for aspiring data scientists.
C++ List Tutorial - Real Examples, Sorting, and When to Use
Learn C++ lists with practical examples, from basics to advanced usage in game matchmaking systems. Covers creation, manipulation, sorting, and comparison with vectors.
Learn R Programming in 7 Hours - Statistical Programming and Data Analysis
Comprehensive R programming tutorial covering installation, environment, data types, structures, functions, loops, and advanced topics for statistical analysis and data manipulation.
Exploit Parallelism for AI Workloads with WASM and OpenMP
Explore optimizing AI workloads using WebAssembly and OpenMP for multithreaded, shared memory, and vectorized operations, focusing on challenges and benefits in cloud computing environments.
Mathematical Problems in Machine Learning - Part 1 of 4
Dive into mathematical probability fundamentals for machine learning through Stanford expert Andrea Montanari's comprehensive exploration of key theoretical concepts and applications.
Mathematical Problems in Machine Learning - Lecture 3
Delve into advanced mathematical probability concepts and their applications in machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration.
Mathematical Problems in Machine Learning - Lecture 2
Delve into mathematical probability fundamentals and their applications in machine learning through advanced theoretical concepts and problem-solving approaches from Stanford expertise.
Mathematical Problems in Machine Learning - Lecture 4
Delve into advanced mathematical probability concepts applied to machine learning through Stanford expert Andrea Montanari's comprehensive theoretical exploration and analysis.
Matrix Long Short-Term Memory (mLSTM) - A New Alternative to Transformer LLMs
Explore the innovative xLSTM architecture and its matrix-based approach to neural networks, comparing its potential advantages over traditional transformer models in language processing.
Ring Attention and Blockwise Transformers for Extended Context Length in Language Models
Dive into the technical mechanics of Ring Attention, exploring how it achieves million-token context lengths in large language models through blockwise parallel transformers and efficient implementation.
Parameter Efficient Fine-Tuning with Multiple LoRA Adapters for Large Language Models
Dive into parameter efficient fine-tuning with LoRA adapters, exploring matrix factorization, SVD, and advanced configurations for optimizing large language models.
Mamba AI: Understanding Selective State Space Models as Transformer Alternatives
Explore the groundbreaking MAMBA architecture and its potential to revolutionize AI through selective state space models, offering a compelling alternative to traditional transformers.
The Art of Text to Vector Transformation - A Comprehensive Look at AI and NLP Transformers
Dive into the fundamentals of converting text to mathematical vectors, understanding tokenization methods like BERT and BPE for NLP applications and AI transformer models.
Eigenpath Traversal by Poisson-Distributed Phase Randomisation
Explore quantum computation through Poisson-distributed phase randomization, examining how dephasing operations enable eigenspace tracking and optimize quantum algorithms like Grover search.
A Shortcut to a Near-Optimal Quantum Linear System Solver
Discover a simplified approach to quantum linear system solving that achieves near-optimal performance through kernel reflection, offering improved efficiency for practical applications in quantum computing.
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.