How to Transform and Improve Your Deep Learning Code to a Visual Neural Network
Learn to visualize and improve deep learning neural networks, making them easier to understand and explain through code transformation and network visualization techniques.
Predicting Wildfire Hotspots Using California Wildfire Data
Learn to predict wildfire hotspots using California data. Covers data collection, feature engineering, ML modeling with LightGBM/XGBoost/H2O.ai, and visualization techniques for effective wildfire risk assessment.
A Deep Dive into Machine Learning and MLOps Pipeline with Model Registry and Feature Store
Explore key components of modern ML pipelines: AutoML, observability, model registry, transformation, and feature store. Learn to build scalable, reliable MLOps systems for enterprise-level machine learning.
A Visual Introduction to Modern and Scalable Machine Learning Pipeline
Visual guide to modern, scalable machine learning pipelines: from data prep to deployment. Covers key stages like training, validation, inference, feature stores, and model registry. Ideal for understanding ML infrastructure evolution.
Build a Neural Network for Classification from Scratch with PyTorch
Learn to build a neural network for classification using PyTorch, covering data preparation, network structure, and implementation. Gain hands-on experience in deep learning fundamentals.
Workshop - I Didn't Know I Could Do That with Docker - AWS ECS Integration
Explore Docker's integration with AWS ECS using Fargate in this hands-on workshop, covering new features, Docker Compose, Cloud Formation, logging, Grafana, and more.
LoRA vs Dreambooth vs Textual Inversion vs Hypernetworks
Comprehensive comparison of AI image generation techniques, analyzing research papers and real-world data to determine the most effective method for teaching concepts to Stable Diffusion.
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.
Chatbot with LangChain Agents + GPT 3.5
Learn to build a conversational agent using LangChain and GPT-3.5, leveraging vector search retrieval to provide context from Lex Fridman's podcast for intelligent responses.
Data Management - FSDL 2022
Comprehensive overview of data management in ML, covering storage, processing, exploration, and versioning. Includes best practices and insights on self-supervised learning and data labeling.
Augmented Language Models - LLM Bootcamp
Explore techniques for enhancing language models with external context: retrieval augmentation, chaining, and tool use. Learn about embeddings, databases, and practical applications in AI development.
Project Walkthrough - AskFSDL - LLM Bootcamp
Walkthrough of building an LLM-powered Discord bot for answering questions about neural network applications, covering tooling, data cleaning, infrastructure, frontend, embeddings, and monitoring.
TorchVision Quick Start - Building an Image Classifier in 10 Minutes
Build an image classifier for orangutans and komodo dragons using PyTorch's torchvision library and a pretrained ResNet model. Learn to leverage pre-trained models for quick and effective computer vision tasks.
Building Recommender System in PHP8
Develop a fully functional Movie Recommendation Engine using PHP8 and Kaggle datasets. Learn recommendation system basics and create a customizable solution for personalized movie suggestions.
Recent Advances in Diversity Maximization in the Offline and Composable Coreset Models
Explore recent advancements in diversity maximization for data summarization and recommendation systems, focusing on offline and composable coreset models for handling massive datasets efficiently.
Analyze Data with PowerShell - U.S. Presidential Election 2020 Dataset
Learn to analyze U.S. Presidential Election 2020 data using PowerShell. Download JSON data, convert it to objects, filter results, and add calculated properties for insightful data exploration.
Machine Learning Fundamentals with Python and R
Comprehensive introduction to machine learning fundamentals using Python and R, covering linear regression, data types, polynomial regression, and NumPy, with hands-on projects and practical applications.
Generative Models Application in High Energy Physics
Explore generative models in high-energy physics, focusing on applications and techniques for data analysis and new physics discovery in particle collider experiments.
Generative Models in High Energy Physics
Explore generative models in high-energy physics, focusing on applications for data analysis and new physics discovery. Learn techniques to handle large datasets and improve experimental outcomes.
Neural Simulation-based Inference - Lecture 5
Explore neural simulation-based inference techniques for high energy physics data analysis, focusing on advanced machine learning applications in particle physics research.
Lectures on Deepsets, Graph Neural Networks and Transformers with Applications - Lecture 6
Explore advanced deep learning architectures like Deepsets, Graph Neural Networks, and Transformers, with applications in high energy physics data analysis and research.