Neural Networks
New research explores optimization algorithms for large-scale neural network training, examining gradient descent variants and convergence strategies critical to modern AI systems.
mechanistic interpretability
Researchers introduce SALVE, combining sparse autoencoders with latent vector editing for precise mechanistic control over neural network behaviors and outputs.
AMD
AMD researchers unveil AIE4ML, an end-to-end compiler framework that maps neural networks to next-gen AI Engines, achieving significant speedups over CPU implementations for ML workloads.
Deep Learning
New research demonstrates that deep neural networks exhibit phase transitions during training, revealing hierarchical feature organization that could reshape how we understand and design AI architectures.
AI Agents
Learn to build AI agents that learn, store, and reuse skills as modular neural components. This technical guide covers procedural memory architecture for persistent skill acquisition.
transformers
Deep technical comparison of transformer and mixture of experts architectures, exploring how MoE models achieve computational efficiency while maintaining performance in modern AI systems including video generation.
Neural Networks
Researchers develop breakthrough verification method that accelerates neural network explanation validation by an order of magnitude, enabling more reliable AI systems for content authentication and deepfake detection applications.
Machine Learning
Essential linear algebra concepts that power machine learning models, from vectors and matrices to eigenvalues. A technical deep dive into the mathematical foundations underlying AI systems including neural networks and transformers.
transformers
Learn to implement transformer components and mini-GPT models from the ground up using Tinygrad. This technical deep dive covers attention mechanisms, layer normalization, and neural network fundamentals to understand how modern AI systems work.
Machine Learning
Tensors are the fundamental data structures powering modern AI systems. This technical deep dive explains how these mathematical objects enable neural networks to process images, video, and audio for generation and manipulation.
PyTorch
Deep dive into PyTorch neural network development, from manual gradient computation to leveraging nn.Module and optim for efficient training. Technical tutorial covering implementation fundamentals for modern deep learning.
Machine Learning
Information theory provides the mathematical foundation for modern AI systems. Understanding entropy, KL divergence, and mutual information is essential for grasping how neural networks learn and generate synthetic content.