From-scratch DL framework (AI-4-Alan)

My from-scratch deep-learning framework — where I rebuild things from first principles to actually understand them. Its spine is one generic data interface: classification, detection and reinforcement learning all flow through a single Dataset abstraction, pushed so far that a Gym RL environment is itself wrapped as a Dataset — the same dataloader and training loop then drive supervised and RL runs alike. Built on myconf, a hand-written type-driven config system (metaclass coercion, lazy computed fields, no Pydantic), with tensor-subclass modalities (image/text/bbox) and hand-reimplemented ResNet · VGG · ViT · DETR (Hungarian matching) plus the DQN family. Ships a small scalar autograd with graph visualization, written to relearn backprop. ~11k LOC — openly a learning project. Below: an Atari Breakout rollout — the Gym RL environment wrapped as a Dataset — plus the 84×84 grayscale observation the pipeline feeds the agent.