Yannic Kilcher Courses

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Explore Switch Transformers: a novel approach to scaling AI models to trillion parameters using sparsity and hard routing, offering improved speed and efficiency compared to dense counterparts.

• YouTube
• 34 minutes
• On-Demand
• Free Video

OpenAI CLIP- Connecting Text and Images

Detailed explanation of OpenAI's CLIP model, which connects text and images using contrastive learning on 400M image-text pairs, enabling zero-shot transfer to various computer vision tasks.

• YouTube
• 48 minutes
• On-Demand
• Free Video

OpenAI DALL·E - Creating Images from Text - Blog Post Explained

Explore OpenAI's DALL·E model, its architecture, capabilities, and limitations in generating high-quality images from text descriptions. Learn about its potential impact on AI applications combining text and visuals.

• YouTube
• 56 minutes
• On-Demand
• Free Video

ReBeL - Combining Deep Reinforcement Learning and Search for Imperfect-Information Games

Explores ReBeL, an AI framework combining reinforcement learning and search for imperfect-information games. Explains its application to poker, convergence to Nash equilibrium, and superhuman performance with minimal domain knowledge.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs - Paper Explained

Explores how predictive coding can approximate backpropagation in neural networks, bridging neuroscience and machine learning. Discusses implications for biologically plausible deep learning algorithms.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Fourier Neural Operator for Parametric Partial Differential Equations - Paper Explained

Explore a novel neural network architecture for solving partial differential equations efficiently. Learn about Fourier Neural Operators and their potential to revolutionize scientific computing and engineering simulations.

• YouTube
• 1 hour 6 minutes
• On-Demand
• Free Video

Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

Interview exploring Virtual Outlier Synthesis for out-of-distribution detection in neural networks. Discusses motivations, methodology, and key findings of novel approach to improve model robustness using synthesized outlier data.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Learning What You Don't Know by Virtual Outlier Synthesis - Paper Explained

Explore Virtual Outlier Synthesis, a novel method for improving out-of-distribution detection in deep learning models by generating synthetic outliers during training to enhance decision boundary regularization.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Spurious Normativity Enhances Learning of Compliance and Enforcement Behavior in Artificial Agents

Explore how arbitrary social norms enhance learning of rule enforcement in AI agents, with insights on societal implications and potential applications in artificial general intelligence.

• YouTube
• 1 hour 37 minutes
• On-Demand
• Free Video

AI and Formal Math - Formal Mathematics Statement Curriculum Learning

Explore AI's potential in formal mathematics through expert iteration, curriculum learning, and language modeling. Discover how machines and humans can collaborate to solve complex mathematical problems.

• YouTube
• 58 minutes
• On-Demand
• Free Video

AlphaCode - With the Authors

Explore AlphaCode's development, capabilities, and future potential in competitive programming through an in-depth interview with its creators, covering technical aspects and real-world applications.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Can Wikipedia Help Offline Reinforcement Learning - Author Interview

Exploring the potential of pre-trained language models in offline reinforcement learning, discussing experimental results, model performance, and future directions for leveraging sequence modeling techniques in RL tasks.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Can Wikipedia Help Offline Reinforcement Learning? - Paper Explained

Exploring how pre-training on Wikipedia improves offline reinforcement learning models, enhancing performance, reducing parameters, and revealing connections between language and RL domains.

• YouTube
• 39 minutes
• On-Demand
• Free Video

All About AI Accelerators - GPU, TPU, Dataflow, Near-Memory, Optical, Neuromorphic & More

Comprehensive exploration of AI accelerator technologies, from GPUs to emerging innovations like neuromorphic computing, discussing their principles, advantages, and future potential in advancing artificial intelligence.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Video

CM3- A Causal Masked Multimodal Model of the Internet

Explore CM3, a groundbreaking multimodal model that processes HTML, text, and images. Learn about its innovative training strategy and potential applications in various AI tasks.

• YouTube
• 1 hour 24 minutes
• On-Demand
• Free Video