16 tracks · SQL · pandas · Python
Practice makes perfect
Practice Hub
Think like the business, then answer it three ways — SQL, pandas & Python, side by side. Thousands of browser-graded questions; the Data Skills ladders are free.
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25 projects · 20 run in-browser
Build · Prove · Portfolio
Projects
Build real ML models end to end — supervised, unsupervised, deep learning & paper reproductions. Every notebook is verified to run; most run right in your browser. 3 free to try.
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Interactive Playgrounds
Don’t just read it, turn the knob.
Every big idea here has a live playground: drag a slider, watch the output move, feel the intuition. 93 interactive labsincluding 18 landmark AI papers rebuilt as live demos (DQN, GANs, diffusion, LoRA, RoPE, Mixture-of-Experts, speculative decoding, RLHF…), plus tokenizers, attention, the KV-cache, loss functions, RAG scoring, quantization and system design.
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Gradient Descent
How a model actually learns, ride the loss curve downhill, pick the step size.
FREE 🌡️Temperature & Sampling
Drag one knob and watch an AI choose its next word, safe, or unhinged.
FREE 🔤Tokenizer
Watch text shatter into tokens, and why it costs money.
▶ Free 👀Attention
See which words a transformer looks at, live.
▶ Free 🔺Binary Heap & Priority Queue
Push bubbles up, pop-min sifts down, the O(log n) structure behind every priority queue.
▶ Free 🌲Trie & Autocomplete
Store words as a tree of characters, shared prefixes once, every completion in O(L).
▶ Free 🎯Quickselect & the k-th Largest
Partition around a pivot, recurse into one half, the k-th largest in O(n), no full sort.
▶ Free 🧭Dijkstra’s Shortest Path
Settle the nearest node, relax its edges, watch the shortest route turn out to be the long way round.
▶ Free 👑N-Queens & Backtracking
Try a choice, hit a wall, back up, watch queens solve the board by backtracking.
▶ Free 🧭Word2Vec
king − man + woman = queen, do algebra on meaning.
🔒 Pro 🏁Optimizer Race
SGD, Momentum, RMSProp and Adam race down the same loss surface.
🔒 Pro 📉Cross-Entropy Loss
Why one confident mistake costs more than a hundred hesitant ones.
🔒 Pro 🎲Dropout
Switch off neurons at random, and the net stops overfitting.
🔒 Pro 📐BatchNorm vs LayerNorm
Same matrix, perpendicular axes, and why transformers pick LayerNorm.
🔒 Pro 🎭GANs
A forger and a critic in an arms race, until the fakes look real.
🔒 Pro 🌬Diffusion
Generate images by denoising pure noise, one step at a time.
🔒 Pro 🎮Deep Q-Learning
An agent learns to act from reward alone, the idea behind game-playing AI.
🔒 Pro 👍RLHF Reward Model
Turn “this answer is better” into a number a model can optimize.
🔒 Pro 🔗Self-Attention from Scratch
Five words, three vectors each, watch them decide who to listen to.
🔒 Pro 🎭Multi-Head Attention
Run attention several times in parallel, the same word routed two ways at once.
🔒 Pro 📍Positional Encoding
Attention is order-blind, so stamp every position with a sine/cosine fingerprint.
🔒 Pro 🎡Rotary Embeddings (RoPE)
Encode position by rotation, the attention score then depends only on relative distance.
🔒 Pro 🧱Inside a Transformer Block
Kill residuals or LayerNorm and watch a 48-layer net explode.
🔒 Pro 🗃️The KV-Cache
Why LLM text generation is O(n), not O(n²), cache the past.
🔒 Pro 🔀Mixture of Experts
Route each token to just its top-k experts, a trillion params, a fraction of the compute.
🔒 Pro 🗜️Quantizing a Model
Shrink a 14 GB model to 3.5 GB, then rescue the outliers.
🔒 Pro 🪜LoRA Fine-Tuning
Fine-tune with two skinny matrices, 600× fewer params.
🔒 Pro 🗡Beam Search vs Greedy
Greedy grabs the top word and gets trapped, beam keeps the top-b paths and finds a better sentence.
🔒 Pro ⏩Speculative Decoding
A small draft model makes a big one 2–4× faster, provably identical output.
🔒 Pro 🎯Scoring a RAG Pipeline
Watch a RAG answer stop hallucinating as recall completes.
🔒 Pro 🔃Consistent Hashing
Add a server, move 1/N of the keys, not 90%.
🔒 Pro 🌸Bloom Filters
A set in 32 bytes that never says “no” by mistake.
🔒 Pro
▶ Explore all 74 playgrounds
38 free · 36 Pro, no install, just your browser