- Executors
-
Learn
Learn Python Runnable examples, NumPy, pandas, and more Learn AINeural networks, ML and LLMs from scratch Learn C & C++ Pointers, memory, STL, and modern C++ Learn C#C# basics, OOP, LINQ, async, and .NET exercises Python Algorithms Search, sorting, recursion, and visualizers O(n) C & C++ Algorithms Compare algorithms implemented in C and C++
- ChallengesNew
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
- Python · C++ · C#
- Automated tests
- AI code review
- Community solutions
- 226
- Challenges
- 2
- Solutions shared
- 3
- Languages
All challenges
Search by topic, language, or difficulty.
Medium
Gradients of a Single Neuron
Use the chain rule to find how the loss changes with each weight and the bias of a sigmoid neuron.
You'll learn
Chain ruleGradients
No solutions yet
Easy
Sum an Array
Add up all values in a list.
You'll learn
AccumulatorsEmpty-list cases
No solutions yet
Easy
Difference of Squares
Subtract the sum of squares from the square of the sum.
You'll learn
FormulasArithmetic
No solutions yet
Easy
Loss Functions: MSE, MAE and Cross-Entropy
Measure how wrong a model's predictions are with mean squared error, mean absolute error or binary cross-entropy.
You'll learn
MSECross-entropy
No solutions yet
Easy
Single Artificial Neuron
Compute the output of one neuron: weighted sum of the inputs plus a bias, passed through sigmoid.
You'll learn
NeuronsWeighted sums
No solutions yet
Medium
Layer Normalization
Normalize each row to zero mean and unit variance, then scale and shift it, as every Transformer block does.
You'll learn
Layer normTransformers
No solutions yet
Hard
Backpropagation in a Two-Layer Network
Compute every weight and bias gradient of a network with one sigmoid hidden layer and one sigmoid output.
You'll learn
BackpropagationChain rule
No solutions yet
Medium
Digital Root
Sum the digits repeatedly until one digit remains.
You'll learn
Nested loopsDigit sums
No solutions yet
Easy
Derivatives of Activation Functions
Compute the derivative of sigmoid, ReLU, Leaky ReLU or tanh at each value, the slopes backpropagation needs.
You'll learn
DerivativesBackpropagation
No solutions yet
Medium
Digit Power Sum
Add each digit raised to its position from the left.
You'll learn
Digit extractionPowersLoops
No solutions yet
Easy
Markov Chain State Distribution
Propagate a probability distribution through a Markov chain's transition matrix for a number of steps.
You'll learn
Markov chainsProbability
No solutions yet
Medium
Dense Layer Forward Pass
Compute the outputs of a fully connected layer of neurons with a choice of activation.
You'll learn
Dense layersActivations
No solutions yet
Easy
Chess Square Color
Determine the color of a square on an 8 by 8 board.
You'll learn
CoordinatesParity
No solutions yet
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
Medium
Mini-Batch Gradient Descent
Train a linear model by updating the weights after every small batch of samples instead of the whole dataset.
You'll learn
Mini-batchesGradient descent
No solutions yet
Easy
Hours, Minutes, and Seconds
Convert a duration in seconds to a compact readable string.
You'll learn
Time unitsString formatting
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
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
Browse by topic
- Beginner 95
- Ai 47
- Machine Learning 42
- Strings 36
- Arrays 31
- Math 21
- Simulation 19
- Neural Networks 17
- Numbers 16
- Advanced 15
- Dynamic Programming 12
- Parsing 12
- Competitive Programming 10
- Conditions 9
- Design Patterns 8
- Graphs 8
- Grids 8
- Loops 8
- Recursion 7
- Combinatorics 6
- Number Theory 6
- Sorting 6
- Functions 5
- Greedy 5
- Nested Loops 5
- Sequences 5
- Stacks 5
- Variables 5
- Algorithms 4
- Data Structures 4
- Geometry 4
- Patterns 4
- Dictionaries 3
- Fractions 3
- Reinforcement Learning 3
- Sliding Window 3
- Trees 3