Efficient Decentralized Federated Singular Vector Decomposition
Innovative system for efficient decentralized federated SVD, enhancing privacy and performance in distributed applications. Outperforms existing methods with significant speed improvements and larger throughput.
Spectral Methods Meets Asymmetry: Two Recent Stories - Lecture 4
Explore spectral methods for asymmetric data matrices, covering top-K ranking and matrix de-noising. Learn advantages over traditional approaches in data analysis and estimation.
Lossy Compression in Seismology - Improving Efficiency and Speed
Explore lossy compression techniques in seismology to manage large datasets, improve storage, and accelerate analysis without significant loss of accuracy or event detection capabilities.
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.
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.
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.
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.
Master AI Efficiency with LoRA - Optimize Fine-Tuning for Large Language Models
Optimize AI fine-tuning with LoRA: Learn selective parameter updates, preserve model integrity, and adapt models efficiently across tasks. Gain practical insights for streamlined, resource-efficient AI projects.
Spectral Analysis of LoRA vs Full Fine-Tuning in Language Models
Delve into the technical comparison between LoRA and full fine-tuning methods for language models, exploring their structural differences, spectral properties, and impact on model performance.
Random Projection in Data Mining - Spring 2023
Explore random projection techniques in data mining, from PCA and SVD fundamentals to Johnson-Lindenstrauss lemma, with practical algorithms and theoretical foundations.
Building Decision-Making Assistants: Introduction to Recommendation Systems
Explore recommendation systems from basic classification to advanced Netflix-style matrix factorization, covering content filtering, collaboration models, SVD, and PCA techniques.
Singular Value Decomposition and Rank-K Approximation - Lecture 19
Dive into Singular Value Decomposition (SVD) concepts, exploring vector path tracing and rank-k approximation techniques for advanced matrix analysis and dimensionality reduction.
Dimensionality Reduction: Rank-k Approximation and Eigen-decomposition - Lecture 20
Dive into advanced dimensionality reduction techniques, exploring rank-k approximation, eigen-decomposition, and power method applications in data analysis.
Data Mining: Frequent Directions and Random Projections - Spring 2023
Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.
Spectral Learning
Explore spectral learning techniques and their applications in machine learning and data analysis with Lorenzo Chicchi's comprehensive lecture.