Random Forest from Scratch
Build a complete Random Forest classifier from impurity math to a bagged ensemble with bootstrapping and feature subsampling.
Start project →Every project is open. Start anywhere, follow your curiosity, and learn at your own pace.
Build a complete Random Forest classifier from impurity math to a bagged ensemble with bootstrapping and feature subsampling.
Start project →Build a linear SVM using hinge loss, a regularized objective, and gradient-based optimization.
Start project →Assemble a LeNet-style convolutional network with im2col convolutions, gradients, Adam, and a complete training loop.
Start project →Reimplement the original encoder-decoder Transformer with multi-head attention, scheduling, and beam search.
Start project →Grow a character-level language model from a bigram baseline into a working GPT with multi-head attention and sampling.
Start project →Train a tabular Q-learning agent with epsilon-greedy exploration and greedy evaluation.
Start project →Build the game engine, policy-value network, PUCT MCTS, self-play generation, training, and baseline evaluation.
Start project →Construct ingestion, chunking, embeddings, hybrid retrieval, grounded generation, evaluation, and conversational memory.
Start project →Implement log-prob utilities, Bradley–Terry preferences, DPO gradients, IPO variants, and a full evaluation pipeline.
Start project →Build decoding, SFT, LoRA, reward modeling, PPO, preference optimization, evaluation, and a model comparison interface.
Start project →Build a ViT encoder, multimodal projector, causal decoder, training loop, and caption generation from raw tensor operations.
Start project →Build a tiled, IO-aware Flash Attention kernel with online softmax and causal masking.
Start project →Implement reductions, activations, fused RMSNorm, Softmax, RoPE, and SwiGLU kernels for efficient inference.
Start project →Construct sampling, tokenization, KV caching, paged allocation, continuous batching, streaming, and benchmarking.
Start project →Wrap cached GPT inference in Merkle commitments and spot checks, then simulate a decentralized committee with rewards and slashing.
Start project →Implement initialization, forward passes, loss, autodiff, and pure functional SGD updates in JAX.
Start project →Construct a lazy buffer, reverse-mode autodiff engine, tensor API, neural primitives, and train a small MLP.
Start project →Implement client partitioning, local SGD, weighted aggregation, partial participation, and non-IID experiments.
Start project →Train workers locally, aggregate pseudo-gradients with an outer optimizer, and quantify communication savings.
Start project →Implement accumulation, checkpointing, mixed precision, all-reduce, and ZeRO-style optimizer sharding.
Start project →Build CUDA kernels for a complete sparse mixture-of-experts forward, backward, and training loop.
Start project →Train a VQ-VAE image tokenizer and an autoregressive text-conditioned transformer with guided sampling.
Start project →Load a 4-bit Qwen model, attach LoRA adapters, format instructions, run SFT, and generate with the tuned model.
Start project →Build proportion tests, sample sizing, multiple-testing corrections, difference-in-differences, and synthetic control.
Start project →Build expected-value games and a quoting engine that manages inventory, adverse selection, and P&L.
Start project →Build the game engine, minimax, tabular self-play, and a DQN agent, then compare value and policy learners.
Start project →