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C# coding challenges
226 challenges you can solve in C#, from first loops to graphs, dynamic programming and machine learning. Write your code in the browser, run the tests instantly and get an AI review.
Easy · 134 Medium · 68 Hard · 24
Skills you'll practise: CountingDigit extractionString splitting2D listsArithmeticFormulasListsLoopsConditionsDivisibilityFloating pointGradient descent
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All challenges
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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
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
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
Easy
Flatten a 2D Array
Combine the rows of a 2D integer array into one array.
You'll learn
2D arraysNested loops
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
Medium
Byte Pair Encoding (BPE) Merges
Learn subword merges the way GPT tokenizers do: repeatedly merge the most frequent pair of adjacent symbols.
You'll learn
BPESubword tokenization
No solutions yet
Easy
Cosine Similarity
Measure how similar two vectors are by the angle between them, as search engines do with embeddings.
You'll learn
Vector similarityEmbeddings
No solutions yet
Easy
Logistic Regression Prediction
Classify samples as 0 or 1 by passing a weighted sum through the sigmoid and comparing it to a threshold.
You'll learn
Logistic regressionThresholds
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
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
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
Easy
Categorical Cross-Entropy Loss
Measure the loss of a multi-class classifier from its predicted probabilities and the true classes.
You'll learn
Classification lossLogarithms
No solutions yet
Medium
Sinusoidal Positional Encoding
Compute the sine and cosine position vectors Transformers add to token embeddings.
You'll learn
Positional encodingTrigonometry
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
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
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
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