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Ai coding challenges
Practise ai with 47 coding challenges, from easy to hard. Every challenge runs in your browser with instant tests, an AI review of your code and community solutions to compare.
Easy · 19 Medium · 24 Hard · 4
Skills you'll practise: Gradient descentBackpropagationChain ruleDecision treesRegressionTransformersA* searchActivationsAdamAlpha-beta pruningAttentionBPE
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All challenges
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Medium
Minimax Tic-Tac-Toe
Pick the perfect tic-tac-toe move by searching the whole game tree with minimax.
You'll learn
MinimaxGame trees
No solutions yet
Easy
Gradient Descent with Momentum
Apply momentum updates, which keep part of the previous step so training speeds up along consistent directions.
You'll learn
MomentumOptimizers
No solutions yet
Easy
Xavier and He Weight Initialization
Compute the range or standard deviation used to initialize the weights of a layer.
You'll learn
Xavier and He initTraining stability
No solutions yet
Hard
Train a Neural Network to Solve XOR
Combine everything: train a 2-2-1 sigmoid network with backpropagation until it learns XOR.
You'll learn
Neural network trainingXOR problem
No solutions yet
Medium
Adam Optimizer
Implement Adam, the default optimizer for deep learning, with moving averages and bias correction.
You'll learn
AdamBias correction
No solutions yet
Hard
Multi-Head Self-Attention
Project the input into queries, keys and values, run attention in several heads in parallel, and combine them.
You'll learn
Multi-head attentionMatrix projections
No solutions yet
Easy
Softmax Function
Turn raw scores into probabilities that add up to 1, without overflowing on large values.
You'll learn
SoftmaxNumerical stability
No solutions yet
Medium
Train a Neuron with Gradient Descent
Repeat forward pass, gradients and weight updates to teach a sigmoid neuron from examples.
You'll learn
Gradient descentTraining loops
No solutions yet
Medium
Naive Bayes Classifier
Predict a class from categorical features with Bayes' theorem and Laplace smoothing.
You'll learn
Bayes' theoremLaplace smoothing
No solutions yet
Medium
Simple Linear Regression (Least Squares)
Fit the best straight line through data points with the least squares formulas.
You'll learn
Least squaresRegression
No solutions yet
Easy
Gini Impurity
Measure how mixed the labels in a group are, the score decision trees use to choose splits.
You'll learn
Decision treesImpurity measures
No solutions yet
Medium
K-Means Clustering
Group points into k clusters by repeatedly assigning them to the nearest centroid and moving the centroids.
You'll learn
ClusteringCentroids
No solutions yet
Easy
Z-Score Standardization
Center a feature at 0 and scale it to a standard deviation of 1.
You'll learn
StandardizationMean and std dev
No solutions yet
Easy
One-Hot Encoding
Turn categories into vectors with a single 1, the input format for classifiers and embeddings.
You'll learn
One-hot vectorsCategorical data
No solutions yet
Easy
Min-Max Normalization
Rescale a feature so its smallest value becomes 0 and its largest becomes 1.
You'll learn
Feature scalingData preprocessing
No solutions yet
Medium
Temperature and Top-K Sampling
Choose the next token like a language model does: keep the top k logits, apply temperature, and sample.
You'll learn
Text generationSampling
No solutions yet
Medium
Information Gain
Compute how much splitting on a feature reduces entropy, the criterion behind ID3 decision trees.
You'll learn
EntropyDecision trees
No solutions yet
Medium
Linear Regression with Gradient Descent
Learn the slope and intercept of a line by repeatedly stepping down the mean squared error.
You'll learn
Gradient descentRegression
No solutions yet
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