Yannic Kilcher Courses

LLaMA- Open and Efficient Foundation Language Models - Paper Explained

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Detailed explanation of Meta's LLaMA language models, covering training data, architecture, implementation, and performance. Explores how smaller models can outperform larger ones with optimized training.

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

Training More Effective Learned Optimizers, and Using Them to Train Themselves - Paper Explained

Explores a novel approach to machine learning optimization using neural networks, discussing its architecture, training process, and potential to revolutionize model training through self-improvement and generalization.

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

The Hardware Lottery - Paper Explained

Explores the "hardware lottery" concept in AI research, discussing how hardware availability influences algorithmic success, with historical examples and future implications for machine learning progress.

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

Assessing Game Balance with AlphaZero - Exploring Alternative Rule Sets in Chess

Explore how AlphaZero assesses chess variants, analyzing game dynamics and strategic changes resulting from rule modifications. Gain insights into AI's potential for game design and balance evaluation.

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

Learning to Summarize from Human Feedback

Explore OpenAI's innovative approach to text summarization using human feedback and reinforcement learning, improving summary quality beyond traditional metrics and supervised learning methods.

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

Self-Classifying MNIST Digits - Paper Explained

Explore how neural cellular automata can achieve global consensus through local communication, applied to digit classification. Discover innovative approaches to modeling biological systems and machine learning tasks.

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

Axial-DeepLab - Stand-Alone Axial-Attention for Panoptic Segmentation

Explore a novel approach to image processing using axial-attention, combining positional embeddings and factorized self-attention to achieve state-of-the-art results in image segmentation tasks.

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

Radioactive Data - Tracing Through Training

Explore a novel technique for detecting dataset usage in model training through imperceptible "radioactive" data markers, offering robust identification even with minimal data exposure.

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

Fast Reinforcement Learning With Generalized Policy Updates - Paper Explained

Explanation of a novel reinforcement learning framework that enables agents to transfer knowledge between tasks, potentially solving new problems with zero-shot learning through generalized policy updates.

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

REALM- Retrieval-Augmented Language Model Pre-Training

Explore REALM, a novel approach to language model pre-training that integrates a latent knowledge retriever, enhancing open-domain question answering and offering interpretability and modularity benefits.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Meta-Learning Through Hebbian Plasticity in Random Networks - Paper Explained

Explores biologically-inspired Hebbian learning for adaptive AI agents. Demonstrates how random neural networks can self-organize during runtime to solve RL tasks and adapt to new situations without fixed policies.

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

Hopfield Networks Is All You Need - Paper Explained

Explores the connection between Hopfield networks and transformer attention, analyzing BERT through this lens and applying Hopfield attention to immune repertoire classification.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Big Bird- Transformers for Longer Sequences

Explanation of BigBird's sparse attention mechanism for transformers, enabling longer sequence processing. Covers theoretical foundations, architecture details, and experimental results in NLP and genomics applications.

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

ImageNet Classification with Deep Convolutional Neural Networks - Paper Explained

Detailed explanation of AlexNet, the groundbreaking deep convolutional neural network that revolutionized computer vision and kickstarted the deep learning era in image classification.

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

Generative Adversarial Networks - Paper Explained

Explore the groundbreaking Generative Adversarial Networks paper, covering its innovative approach, theoretical foundations, and practical implications for AI-driven image generation and deep learning advancements.

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