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.
machine learning
Loss functions are the mathematical compass that guides AI model training. Understanding how these optimization tools work—from MSE to cross-entropy—is fundamental to building effective machine learning systems.
machine learning
New research introduces Dynamic Nested Hierarchies (DNH), an architecture enabling ML systems to autonomously evolve their structure during training. Framework addresses catastrophic forgetting in lifelong learning through self-organizing hierarchical components.
computer vision
AI doesn't perceive images like humans do. Understanding tokenization, embeddings, and attention mechanisms reveals how neural networks process visual data—critical knowledge for developing and detecting synthetic media.
Neural Networks
Researchers introduce breakthrough training framework that addresses scalability challenges in neural networks, with implications for large-scale AI video and synthetic media model development through innovative optimization approaches.
LLM
New research introduces gradient-aware approach to select training data that helps large language models retain prior knowledge while learning new information, addressing catastrophic forgetting through intelligent sample selection.