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.
Recurrent Neural Networks, Transformers, and Attention
Explore recurrent neural networks, transformers, and attention mechanisms in deep learning. Learn design principles, applications, and advanced concepts like LSTM and backpropagation through time.
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.
PyTorch's Computational Graph and Torchviz Visualization Tutorial
Learn to build neural networks, understand PyTorch's computational graph, and visualize deep learning networks using PyTorchViz in this comprehensive tutorial.
Carlos Ruiz, Colegio de Matematicas Bourbaki: Tame Neural Networks
Explore inequalities in logically tame neural networks and their role in understanding deep learning, with insights from mathematical analysis.
Particle Methods for Optimization Over Measures - Lecture 3
Explore particle methods for measure optimization in this advanced lecture, covering theoretical foundations and practical applications in data science and machine learning.
Transformers - From Zero to Hero
Comprehensive deep dive into transformer architecture, covering key concepts from self-attention to tokenization. Includes hands-on coding for both encoder and decoder implementations.
Machine Learning Explained - Teaching a Robot to Walk Tutorial
Comprehensive tutorial on machine learning concepts, from handwriting recognition to reinforcement learning, with practical demonstrations using neural networks and robot simulations.
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.
Introduction of BDT and Neural Networks for Classification in High Energy Physics - Lecture 2
Explore Boosted Decision Trees and Neural Networks for particle classification in High Energy Physics, enhancing data analysis techniques for groundbreaking discoveries.
Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 7
Explore advanced machine learning techniques for high energy physics, including Deepsets, Graph Neural Networks, and Transformers, to analyze complex particle physics data.
Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6
Explore differentiable programming for optimizing experiments in high-energy physics, focusing on end-to-end solutions and advanced machine learning techniques.
Neural Simulation-based Inference - Lecture 2
Explore advanced neural simulation-based inference techniques for high energy physics, focusing on applications in data analysis and new physics discovery at particle colliders.
Equal Risk Option Pricing with Deep Reinforcement Learning
Explore innovative option pricing using deep reinforcement learning, focusing on equal risk approaches, hedging strategies, and market dynamics. Gain insights into advanced quantitative finance techniques.
Tropical Solutions to Hard Problems in Auction Theory and Neural Networks - Lecture I
Explore tropical mathematics and its applications in neural networks, auction theory, and more. Learn about min-plus algebra, tropical objects, and polyhedral geometry to solve complex problems.
Generalization in Deep Learning Through the Lens of Implicit Rank Minimization
Explores implicit regularization in deep learning through matrix and tensor factorizations, revealing tendencies towards low ranks. Discusses implications for generalization and potential improvements in neural network design.
Ran Levi- Topological Analysis of Neural Networks
Explore topological analysis of neural networks, from C. elegans to complex brain structures. Discover surprising findings and innovative approaches in computational neuroscience.