Random Forest from Scratch
Build a complete Random Forest classifier from impurity math to a bagged ensemble with bootstrapping and feature subsampling.
A project-first path from classical algorithms to production AI systems. Build, learn, and leave a trail worth sharing.
Complete each project to unlock the next challenge.
Build a complete Random Forest classifier from impurity math to a bagged ensemble with bootstrapping and feature subsampling.
Build a linear SVM using hinge loss, a regularized objective, and gradient-based optimization.
Assemble a LeNet-style convolutional network with im2col convolutions, gradients, Adam, and a complete training loop.
Reimplement the original encoder-decoder Transformer with multi-head attention, scheduling, and beam search.
Grow a character-level language model from a bigram baseline into a working GPT with multi-head attention and sampling.
Train a tabular Q-learning agent with epsilon-greedy exploration and greedy evaluation.
Build the game engine, policy-value network, PUCT MCTS, self-play generation, training, and baseline evaluation.
Construct ingestion, chunking, embeddings, hybrid retrieval, grounded generation, evaluation, and conversational memory.
Implement log-prob utilities, Bradley–Terry preferences, DPO gradients, IPO variants, and a full evaluation pipeline.
Build decoding, SFT, LoRA, reward modeling, PPO, preference optimization, evaluation, and a model comparison interface.
Build a ViT encoder, multimodal projector, causal decoder, training loop, and caption generation from raw tensor operations.
Build a tiled, IO-aware Flash Attention kernel with online softmax and causal masking.
Implement reductions, activations, fused RMSNorm, Softmax, RoPE, and SwiGLU kernels for efficient inference.
Construct sampling, tokenization, KV caching, paged allocation, continuous batching, streaming, and benchmarking.
Wrap cached GPT inference in Merkle commitments and spot checks, then simulate a decentralized committee with rewards and slashing.
Implement initialization, forward passes, loss, autodiff, and pure functional SGD updates in JAX.
Construct a lazy buffer, reverse-mode autodiff engine, tensor API, neural primitives, and train a small MLP.
Implement client partitioning, local SGD, weighted aggregation, partial participation, and non-IID experiments.
Train workers locally, aggregate pseudo-gradients with an outer optimizer, and quantify communication savings.
Implement accumulation, checkpointing, mixed precision, all-reduce, and ZeRO-style optimizer sharding.
Build CUDA kernels for a complete sparse mixture-of-experts forward, backward, and training loop.
Train a VQ-VAE image tokenizer and an autoregressive text-conditioned transformer with guided sampling.
Load a 4-bit Qwen model, attach LoRA adapters, format instructions, run SFT, and generate with the tuned model.
Build proportion tests, sample sizing, multiple-testing corrections, difference-in-differences, and synthetic control.
Build expected-value games and a quoting engine that manages inventory, adverse selection, and P&L.
Build the game engine, minimax, tabular self-play, and a DQN agent, then compare value and policy learners.
Top 18% of learners
At your current pace
5 projects to go