Market Research Courses

Yannic Kilcher

Sparse Is Enough in Scaling Transformers - ML Research Paper Explained

Detailed explanation of Scaling Transformers and Terraformer architecture, focusing on leveraging sparsity to improve efficiency and speed in large language models while maintaining accuracy.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Learning Rate Grafting: Transferability of Optimizer Tuning - Machine Learning Research Paper Review

Explore grafting technique for transferring learning rate schedules between optimizers, improving deep learning model performance and reducing computational costs in hyperparameter tuning.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Gradients Are Not All You Need - Machine Learning Research Paper Explained

Explores limitations of differentiable programming in machine learning, focusing on chaos-based failures in various systems. Discusses alternatives to backpropagation for gradient estimation in complex, stochastic environments.

• YouTube
• 49 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Autoregressive Diffusion Models - Machine Learning Research Paper Explained

Explore Autoregressive Diffusion Models, a novel approach combining autoregressive and diffusion models for efficient, order-agnostic generation and compression of text and image data.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Topographic VAEs Learn Equivariant Capsules - Machine Learning Research Paper Explained

Explore Topographic VAEs: a novel approach to deep generative models with organized latent variables, bridging topographic organization and equivariance in neural networks for improved feature learning and transformation handling.

• YouTube
• 32 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Infinite Memory Transformer - Research Paper Explained

Explores innovative Transformer model with unbounded memory, enabling processing of arbitrarily long sequences. Discusses continuous attention mechanisms, sticky memories, and potential applications in language modeling.

• YouTube
• 37 minutes
• Self-Paced
• Free Video

Yannic Kilcher

PonderNet - Learning to Ponder - Machine Learning Research Paper Explained

Explore DeepMind's PonderNet, a novel approach to adaptive computation in neural networks. Learn how it dynamically allocates computational steps based on problem complexity, improving efficiency and performance.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

Yannic Kilcher

The Dimpled Manifold Model of Adversarial Examples in Machine Learning - Research Paper Explained

Explores the Dimpled Manifold Model to explain adversarial examples in machine learning, challenging existing theories and providing experimental evidence for a new perspective on neural network vulnerabilities.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

Yannic Kilcher

XCiT- Cross-Covariance Image Transformers - Facebook AI Machine Learning Research Paper Explained

Explore XCiT, a novel Transformer architecture for computer vision using cross-covariance attention. Learn about its linear complexity, scalability, and performance across various vision tasks.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Efficient and Modular Implicit Differentiation - Machine Learning Research Paper Explained

Explores a unified framework for implicit differentiation in machine learning, enabling automatic differentiation of optimization problems without manual derivations or loop unrolling, with applications in meta-learning and hyperparameter optimization.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Reward Is Enough - Machine Learning Research Paper Explained

Explores the hypothesis that reward maximization drives intelligence development, discussing its implications for AGI and reinforcement learning, with critical analysis and commentary.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Diffusion Models Beat GANs on Image Synthesis - Machine Learning Research Paper Explained

Explore how Diffusion Models outperform GANs in image synthesis, with improved architecture, classifier guidance, and better sample quality while maintaining distribution coverage.

• YouTube
• 55 minutes
• Self-Paced
• Free Video

Yannic Kilcher

MLP-Mixer - An All-MLP Architecture for Vision - Machine Learning Research Paper Explained

Explores MLP-Mixer, a novel architecture using only multi-layer perceptrons for image classification. Discusses its design, performance, and implications for computer vision beyond CNNs and Transformers.

• YouTube
• 28 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Emerging Properties in Self-Supervised Vision Transformers - Facebook AI Research Explained

Explore Facebook AI's DINO system, combining self-supervised learning with Vision Transformers for impressive image analysis without labels. Learn about its architecture, methodology, and groundbreaking results in computer vision.

• YouTube
• 39 minutes
• Self-Paced
• Free Video

Yannic Kilcher

Mastering Atari with Discrete World Models - Machine Learning Research Paper Explained

Explore cutting-edge reinforcement learning with DreamerV2, a model-based AI agent achieving human-level performance on Atari games using discrete world models and latent space predictions.

• YouTube
• 55 minutes
• Self-Paced
• Free Video