Independent projects — infrastructure, robotics, technology surveys, and corporate research
Category · 10 entries

Machine Learning Primer

From the fundamentals of neural networks through reinforcement learning, object detection and recognition, and generative models, explained through both implementation and math.

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Reinforcement Learning Primer

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September 5, 2026 Machine Learning Primer

Object Detection & Semantic Segmentation Primer — Reading "What Is Where" From an Image

Object Detection draws boxes around things; Semantic Segmentation colors in every pixel by meaning. From the difference between the two, through Instance and Panoptic Segmentation, the lineage from R-CNN to YOLO, DETR and DINO, all the way to their 3D point-cloud counterparts — a systematic tour of the fundamentals of scene understanding.

 September 3, 2026 Machine Learning Primer

Machine Learning Primer: Generative Models

Generative models represent data distributions and create conditional outputs. Learn likelihood, VAE, GAN, diffusion, evaluation, provenance, and safety.

 September 3, 2026 Machine Learning Primer

Machine Learning Primer: Foundations — Designing Data, Loss, and Generalization

Machine learning is not just model selection: define the task, data split, loss, evaluation, and deployment change.

 September 3, 2026 Machine Learning Primer

Machine Learning Primer: Neural Network Basics

Neural networks stack affine transforms and nonlinear activations, then reduce loss by backpropagation and gradient-based optimization.

 September 3, 2026 Machine Learning Primer

Machine Learning Primer: Pose Estimation

Pose estimation infers human or object keypoints and 6D pose from images. This primer covers coordinates, losses, occlusion, evaluation, and safety.

 September 3, 2026 Reinforcement Learning Primer

Reinforcement Learning Primer: Imitation Learning and Inverse Reinforcement Learning

Behavior Cloning, DAgger, and inverse reinforcement learning use demonstrations when rewards are hard to write. This primer covers covariate shift, reward inference, VLA connections, evaluation, safety, and data provenance.

 September 3, 2026 Reinforcement Learning Primer

Reinforcement Learning Basics — MDPs, Bellman Equations, and Exploration for Robots

Reinforcement learning is a closed loop in which an agent chooses actions from observations and maximizes delayed rewards. This primer explains MDPs, value functions, policies, Bellman equations, exploration versus exploitation, reward design, and the path from simulation to a real robot.

 September 3, 2026 Reinforcement Learning Primer

Model-Based Reinforcement Learning and Sim-to-Real

World models, prediction error, MPC, domain randomization, system identification, and safety monitoring for real machines.

 September 3, 2026 Reinforcement Learning Primer

Policy Gradients, PPO, and SAC — Stable Continuous Control for Robots

Policy gradients update a policy directly so that steering, thrust, and joint torque can remain continuous. This article connects Actor-Critic, PPO, and SAC, then covers clipping, entropy regularization, evaluation, and the safety boundary for Sim-to-Real transfer.

 September 3, 2026 Reinforcement Learning Primer

Q-Learning and DQN — From a Q-Table to Deep Reinforcement Learning

Q-learning updates action values with the Bellman optimality target. This primer follows the path from a tabular Q-table to a Deep Q-Network, explaining experience replay, target networks, overestimation, and safe placement in a robot stack.