Generating High-Fidelity HI Maps Using Score-Based Diffusion Models - Sultan Hassan
Exploring score-based diffusion models to generate high-fidelity HI maps, discussing applications in astrostatistics and machine learning for galaxy formation and evolution studies.
SHAP and Shapley Values for Interpretable Machine Learning - Kaggle 30 Days of ML Day 18
Explore SHAP and Shapley Values for interpretable machine learning. Learn local interpretation techniques, Python implementation, and visualization methods for explainable AI.
Interpretable Machine Learning Use-Cases - Kaggle 30 Days of ML Day 15
Explore machine learning explainability, interpretability, and use cases for model insights. Learn about feature importance, debugging, and building trust in AI systems.
How to Become a Practical Data Scientist with Kaggle Learn
Discover a comprehensive, free path to becoming a practical data scientist using Kaggle Learn's step-by-step courses, covering Python, machine learning, data visualization, and more.
Building and Deploying a YouTube Thumbnail Downloader with Python and Streamlit
Learn to create and deploy a Python-based YouTube thumbnail downloader using Streamlit, from concept to live web application. Gain practical experience in building and hosting a useful tool for content creators.
Training My Deadlift Bot With MediaPipe and OpenCV
Learn to create a deadlift bot using MediaPipe and OpenCV in this hands-on live stream, exploring practical applications of computer vision and machine learning for fitness tracking.
Introduction to AI and Machine Learning
Learn the basics of machine learning and data science, explore AI's role in cybersecurity, and discover how to augment your practice with data. Ideal for beginners interested in applying AI to security challenges.
Robust Principal Component Analysis
Explore robust PCA for handling corrupted data in fields like fluid mechanics and image processing. Learn its applications and importance in data-driven science and engineering.
Henry Adams - Topology in Machine Learning
Explore persistent homology in machine learning, covering applications in materials science, computer vision, and agent-based modeling. Learn how topology relates to dataset geometry and explainable AI.
TopoAct - Visually Exploring the Shape of Activations in Deep Learning
Explore the shape of neuron activations in deep learning using topological data analysis. Gain insights into learned representations of image classifiers with TopoAct visualization system.
High-Throughput Spectroscopy and Material Discovery by Beyond-DFT Work and Data-Analysis
Explore high-throughput spectroscopy and materials discovery using advanced computational methods, workflows, and data analysis frameworks for accelerated insights in materials science.
Leveraging Reviews - Learning to Price with Buyer and Seller Uncertainty
Explore pricing strategies in online marketplaces using customer reviews and AI. Learn how sellers can optimize prices while helping buyers estimate product value through relevant feedback.
Lab-in-the-Loop De Novo Antibody Design - What Are We Missing From Machine Learning?
Explore lab-in-the-loop de novo antibody design, focusing on computational aspects. Discover generative modeling, computational oracles, and active learning in molecular systems research.
Universes as Bigdata- From Geometry, to Physics, to Machine-Learning
Explore the intersection of string theory, geometry, and machine learning in physics, examining how AI aids mathematical research across various fields.
Building and Maintaining High-Performance AI
Learn strategies for developing and maintaining high-performance AI models, including testing methods, drift detection, and systematic debugging. Gain insights on model monitoring and iteration for optimal performance.
CLIP, T-SNE, and UMAP - Master Image Embeddings and Vector Analysis
Master image embeddings and vector analysis techniques like CLIP, T-SNE, and UMAP. Learn to cluster MNIST images, detect duplicates, and explore essential concepts in computer vision and data science through hands-on practice.
Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
Explore advanced graph learning techniques using subgraph-based networks for expressive, efficient, and domain-independent applications in data science and signal processing.
An Alternative View on AI: Collaborative Learning, Incentives, Social Welfare, and Dynamics - Lecture 3
Exploring AI beyond autonomous agents: collaborative learning, incentives, and social dynamics in AI systems, emphasizing intelligence as a property of entire systems rather than individual entities.
An Alternative View on AI - Collaborative Learning, Incentives, Social Welfare
Explore a broader AI paradigm focusing on collaborative learning, incentives, and social welfare. Gain insights into AI systems where intelligence emerges from cooperative agent interactions and economic principles.
Financial Analyst App - GPT-4, Streamlit, and Llama-Index Tutorial
Build a financial news app UI with Streamlit, integrating GPT-4 and Llama-Index for advanced analysis and user-friendly interaction. Learn to create a powerful tool for financial insights.
AMBA - Providing Employment to Adults with Developmental Challenges
Empowering adults with developmental challenges through technology-driven employment opportunities, transforming lives and challenging societal perceptions.