machine learning
Understanding gradient descent is essential to grasping how neural networks learn. This foundational optimization algorithm powers everything from deepfake generators to detection systems.
multimodal AI
The human brain seamlessly integrates sight, sound, and touch. Replicating this took a decade of AI research and seven critical innovations that now power today's video and image generation systems.
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 Architecture
From Transformers to GANs, these five foundational architectures form the backbone of AI video generation, deepfake creation, and synthetic media systems that every engineer should understand.
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.
AI Agents
Understanding the critical architectural differences between shallow and deep AI agents—from simple reactive systems to complex multi-layered reasoning frameworks that enable autonomous decision-making and adaptive behavior.
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.
Neural Networks
Dropout is a powerful regularization technique that prevents overfitting by randomly deactivating neurons during training. This counterintuitive approach forces networks to learn robust, distributed representations that generalize better to unseen data.
Neural Networks
Liquid Neural Networks introduce adaptive, continuous-time dynamics that outperform traditional RNNs with fewer parameters. The architecture offers real-time processing capabilities crucial for video analysis and autonomous systems through dynamic state adjustments.
LLM Architecture
Deep dive into the technical progression of large language model architectures, from the foundational Transformer through Mixture of Experts to cutting-edge Mixture of Routers, examining how each innovation addresses scaling and efficiency challenges.