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.
neural networks
Deep dive into batch normalization, the technique that revolutionized neural network training by solving internal covariate shift. Essential knowledge for building stable AI models including video generators and deepfake systems.
Deep Learning
Deep dive into the technical foundations of training neural networks, covering backpropagation, gradient descent, optimization algorithms, and the mathematical principles that power modern AI systems from video generation to deepfakes.
neural networks
Deep dive into activation functions, the mathematical components that enable neural networks to learn complex patterns. Explores ReLU, sigmoid, tanh, and modern variants with technical implementation details.