Practice
Implement the primitives that ML engineering interviews and real systems are built on. Every problem runs in your browser. No setup, no submission queue — just write Python and press Run.
165 problems254 topics10 companies
- Accuracy scoreEasynumpymetricsevaluationclassificationfundamentals4 tests
- AdaBoost fit (decision stumps)Mediumnumpyensembleboostingadaboostclassical-mlclassification4 tests
- Adadelta optimizerMediumnumpyoptimizationoptimizeradaptiveadadelta4 tests
- Adagrad optimizerEasynumpyoptimizationoptimizeradaptiveadagrad4 tests
- Adam optimiser stepMediumnumpyoptimizationadam6 tests · Meta · OpenAI · Anthropic
- Adamax optimizerMediumnumpyoptimizationoptimizeradaptiveadamax4 tests
- AdamW step (decoupled weight decay)Mediumnumpyoptimizationadamwweight-decay5 tests
- Affine coupling layer (normalizing flow)Mediumnumpynormalizing-flowgenerativerealnvpinvertible5 tests
- Autocorrelation functionMediumnumpytime-seriesautocorrelationstatistics5 tests
- Autoencoder forward passEasynumpyautoencoderneural-networkrepresentation-learning4 tests
- Bag-of-words encodingEasynumpynlptext-featuresbag-of-words4 tests
- Basic autograd operationsMediumautogradbackpropagationneural-networkcomputational-graph4 tests
- BatchNorm forward (train + eval modes)Mediumnumpynormalizationbatch-normregularization5 tests · Meta · Google · Nvidia · Tesla
- Beam search decodingMediumnumpydecodingbeam-searchllmsequence5 tests
- Bellman equation for value iterationMediumnumpyreinforcement-learningvalue-iterationbellmanmdp5 tests
- Bernoulli Naive Bayes classifierMediumnumpynaive-bayesclassical-mlclassificationprobability4 tests
- Best Gini-based split (decision tree)Mediumnumpydecision-treeclassical-mlginiclassification4 tests
- Binary classification with logistic regressionEasynumpyclassificationlogistic-regressioninferencedecision-boundary4 tests
- BLEU score (unigram)Mediumnlpmetricsmachine-translationevaluationbleu4 tests
- BPE: apply mergesMediumtokenizationBPEalgorithms8 tests
- Train BPE merges from a corpusHardpythontokenizationbpe6 tests
- Conv2D forward (naive, stride 1, no pad)Mediumnumpycnnconvolutionbuild-cnn5 tests
- Conv2D forward (padding + stride)Mediumnumpycnnconvolutionbuild-cnn5 tests
- Max pooling 2D forwardEasynumpycnnpoolingbuild-cnn6 tests
- Average pooling 2D forwardEasynumpycnnpoolingbuild-cnn6 tests
- Conv2D backward (dx, dW)Hardnumpycnnbackpropbuild-cnn5 tests
- Mini-CNN forward (capstone)Mediumnumpycnnclassificationcapstonebuild-cnn5 tests
- Token embedding lookupEasynumpytransformerembeddingsbuild-gpt5 tests
- Sinusoidal positional encodingEasynumpytransformerpositional-encodingbuild-gpt6 tests
- Scaled dot-product self-attentionMediumnumpytransformerattentionbuild-gpt6 tests · OpenAI · Anthropic · Meta · Google · Cohere
- Causal mask: build + applyEasynumpytransformermaskingbuild-gpt5 tests
- Multi-head split + combineMediumnumpytransformerreshapebuild-gpt6 tests
- Multi-Head Attention (full layer)Mediumnumpytransformermulti-head-attentionbuild-gpt5 tests
- LayerNorm forwardEasynumpytransformernormalizationbuild-gpt5 tests
- Transformer block forward (pre-LN, residual)Hardnumpytransformercapstonebuild-gpt5 tests
- Linear forward (Wx + b)Easynumpyneural-netfundamentalsbuild-nn5 tests · Meta · Google · Amazon · Apple
- ReLU forward + backwardEasynumpyneural-netactivationbackpropbuild-nn5 tests
- Sigmoid forward + backwardEasynumpyneural-netactivationbackpropbuild-nn6 tests
- MSE loss + backwardEasynumpyneural-netlossbackpropbuild-nn6 tests
- Linear backward (chain rule)Mediumnumpyneural-netbackpropcalculusbuild-nn6 tests
- SGD step (in place)Easynumpyneural-netoptimizationsgdbuild-nn6 tests
- Train a 2-layer MLP on XORHardnumpyneural-nettraining-loopcapstonebuild-nn5 tests
- Byte-level UTF-8 tokenizerMediumpythontokenizationunicode6 tests
- Cohen's kappa scoreMediumnumpymetricsevaluationagreementstatistics4 tests
- Confusion matrixEasynumpymetricsevaluationclassification4 tests
- Contrastive lossMediumnumpylossmetric-learningembeddingssiamese4 tests
- Warmup + cosine decay LR scheduleEasynumpytraininglr-scheduleoptimization6 tests
- Covariance matrixEasynumpylinear-algebrastatisticscovariance4 tests
- Cross-entropy gradientMediumcalculusbackpropnumpyfundamentals6 tests
- DBSCAN clusteringMediumnumpyclusteringclassical-mldensity-basedunsupervised4 tests
- Decision tree learning (ID3)Harddecision-treeclassical-mlentropyinformation-gainclassification5 tests
- Dense block with 2D convolutionsMediumnumpycnndensenetconvolutionneural-network5 tests
- Dice lossEasynumpylosssegmentationcomputer-visiondice4 tests
- Diffusion forward processMediumnumpydiffusiongenerativeddpm5 tests
- Divide dataset by feature thresholdEasynumpydecision-treeclassical-mldata-splitting4 tests
- DPO lossMediumnumpyllmalignmentdpopreference-learningloss5 tests
- Dropout layer (forward & backward)Mediumnumpyregularizationdropoutneural-networkbackpropagation6 tests
- Dynamic-Tanh (DyT)Mediumnumpyactivationtransformernormalization-freedyt4 tests
- Early stopping on validation lossEasytrainingregularizationearly-stoppingmodel-evaluation4 tests
- Efficient sparse window attentionMediumnumpyattentiontransformersparselong-context5 tests
- Elastic-Net regression (gradient descent)Mediumnumpyregressionregularizationlassoridgegradient-descent4 tests
- Euclidean distance matrixEasynumpylinear-algebradistancesvectorization4 tests
- Exponential moving average of weightsEasynumpytrainingemaweight-averagingregularization5 tests
- Exponential LR schedulerEasyoptimizationlearning-rateschedulertraining4 tests
- F1 score (binary classification)Easynumpymetricsevaluationfundamentals6 tests
- Fisher-Yates shuffleEasynumpysamplingshufflepermutationalgorithm5 tests
- FlashAttention tiled forwardHardnumpyattentiontransformeronline-softmaxflash-attention6 tests
- Focal loss (multiclass)Mediumnumpylossimbalanceclassification6 tests
- Gaussian process regression (RBF)Hardnumpygaussian-processregressionkernel-methodsbayesian4 tests
- GCN layer (message passing)Mediumnumpygraph-neural-networkgcnmessage-passing5 tests
- GELU backward (tanh approximation)Mediumnumpyactivationbackpropcalculusgelu6 tests
- GELU forward (tanh approximation)Easynumpyactivationtransformergelu6 tests
- Generate random subsets (bootstrap)Easynumpysamplingbootstrapbaggingresampling5 tests
- GMM E-step (responsibilities)Mediumnumpyclusteringgmmemunsupervisedprobability4 tests
- Gradient checkpointing forwardEasynumpytrainingmemory-optimizationgradient-checkpointing4 tests
- Clip gradients by global L2 normEasynumpytrainingstabilityoptimization6 tests
- Graph LaplacianMediumnumpygraphlinear-algebraspectrallaplacian4 tests
- Greedy decoding loopEasynumpydecodingautoregressive5 tests
- Group normalizationMediumnumpynormalizationdeep-learningcnngroup-norm4 tests
- Grouped-query attentionMediumnumpyattentiontransformergqainference5 tests
- GRPO objectiveHardnumpyreinforcement-learninggrporlhfllm5 tests
- He weight initializationEasynumpyinitializationneural-networktrainingrelu4 tests
- Huber & Hinge lossesEasynumpylossregressionsvmrobust4 tests
- Instance normalizationMediumnumpynormalizationcomputer-visionstyle-transferinstance-norm4 tests
- Inverse-transform samplingEasynumpysamplingprobabilitymonte-carlo5 tests
- IoU of bounding boxesEasycomputer-visionobject-detectionmetriciou5 tests
- Jaccard similarityEasynumpymetricssimilaritysetsiou4 tests
- k-fold cross-validationEasynumpymodel-evaluationcross-validationresampling5 tests
- KL divergence (discrete)Easynumpylossinformation-theoryfundamentals6 tests
- K-means: one iterationMediumnumpyclassical-mlclustering6 tests
- k-NN classification (majority vote)Easynumpyclassical-mlclassificationfundamentals6 tests
- KV cache for autoregressive inferenceMediumnumpyattentiontransformerinferencekv-cache4 tests
- KV cache compression (MLA)Hardnumpyattentiontransformerinferencemlakv-cache4 tests
- Label-smoothed cross-entropyMediumnumpylossregularizationclassification5 tests
- Leaky ReLU activationEasynumpyactivationneural-netrelu4 tests
- Learning-rate range testMediumnumpytraininglearning-ratehyperparameter-tuning4 tests
- Linear regression — gradient descentEasynumpyregressionlinear-regressiongradient-descentoptimization4 tests
- Linear regression — normal equationMediumnumpyregressionlinear-regressionclassical-mlleast-squares3 tests
- Lion optimizer stepMediumnumpyoptimizationoptimizerlionsign-momentum4 tests
- Log-softmaxEasynumpysoftmaxnumerical-stabilitylossfundamentals5 tests
- Logistic regression — gradient descentMediumnumpyregressionlogistic-regressionclassificationgradient-descent4 tests
- LoRA forward passEasynumpylorafine-tuningpeftllm5 tests
- Matmul backwardMediumnumpybackpropmatmulcalculus5 tests
- Mean reciprocal rank (MRR)Easyrankingmetricsretrievalevaluation4 tests
- METEOR scoreMediumnlpmetricsmachine-translationevaluationmeteor4 tests
- Multi-class cross-entropy lossEasynumpylossclassificationcross-entropyfundamentals4 tests
- Mutual informationMediumnumpyinformation-theorymutual-informationfeature-selectionstatistics5 tests
- Nesterov accelerated gradientMediumnumpyoptimizationoptimizermomentumnesterov4 tests
- Neural ODE forward EulerMediumnumpyneural-odenumerical-integrationeulergenerative5 tests
- Noisy top-k gatingMediumnumpymoegatingroutingtransformer5 tests
- Orthonormal basis (Gram-Schmidt)Mediumnumpylinear-algebragram-schmidtorthogonalization4 tests
- PageRank (power iteration)Mediumnumpygraphpagerankpower-iterationlinear-algebra4 tests
- Pairwise cosine-similarity matrixEasynumpyembeddingssimilarityretrievalcosine4 tests
- Pearson correlation coefficientEasynumpystatisticscorrelationfundamentals4 tests
- Pegasos kernel SVMMediumnumpysvmkernel-methodsclassical-mlclassification4 tests
- Perplexity from log-probsEasynumpynlplanguage-modelsmetricsevaluation4 tests
- Pointwise mutual information (PMI)Mediumnumpynlpinformation-theorystatisticspmi4 tests
- Policy gradient with REINFORCEMediumnumpyreinforcement-learningpolicy-gradientreinforce5 tests
- PPO clipped objectiveMediumnumpyreinforcement-learningpporlhfpolicy-gradient5 tests
- Precision@k and NDCG@kMediumnumpyrankingmetricsretrievalndcg4 tests
- Precision metricEasynumpymetricsevaluationclassificationfundamentals4 tests
- Principal Component Analysis (PCA)Mediumnumpypcadimensionality-reductionlinear-algebraunsupervised4 tests
- Prioritized experience replayMediumnumpyreinforcement-learningexperience-replaydqnimportance-sampling5 tests
- Q-learning for MDPsMediumnumpyreinforcement-learningq-learningmdpvalue-based4 tests
- Rejection samplingMediumnumpysamplingmonte-carloprobability5 tests
- Repetition penalty (HuggingFace-style)Mediumnumpydecodingsampling6 tests
- Reservoir samplingMediumnumpysamplingstreamingalgorithm5 tests
- Residual block with shortcutEasynumpyneural-networkresnetresidualcnn4 tests
- Ridge regression lossEasynumpyregressionregularizationridgeloss4 tests
- RMSNormEasynumpynormalizationtransformerllmrmsnorm4 tests
- RMSprop optimizerEasynumpyoptimizationoptimizeradaptivermsprop4 tests
- Vanilla RNN cell forwardMediumnumpyrnnsequence-models5 tests
- ROC-AUC from scratchMediumnumpymetricsevaluationclassificationroc-auc4 tests
- Rotary Position Embedding (RoPE)Hardnumpytransformerpositional-encodingrope6 tests
- ROUGE-1 scoreEasynlpmetricssummarizationevaluationrouge4 tests
- Scaled dot-product attentionMediumnumpytransformersattention5 tests
- SELU activationEasynumpyactivationneural-netself-normalizingselu4 tests
- Sequence padding & maskingEasynumpynlpsequencepreprocessingmasking5 tests
- SGD with momentum (one step)Easynumpyoptimizationsgdmomentum5 tests
- Shannon entropyEasynumpyinformation-theoryentropystatistics5 tests
- Singular Value Decomposition (2x2)Hardnumpylinear-algebrasvdmatrix-factorization4 tests
- Softmax backwardMediumnumpybackpropsoftmaxcalculus5 tests
- Softmax from scratchEasynumpyfundamentalsnumerical-stability6 tests · Meta · Google · Amazon · Microsoft · Apple
- Softmax (multinomial) regressionMediumnumpyregressionclassificationsoftmaxmulticlassgradient-descent4 tests
- Sorted polynomial featuresMediumnumpyfeature-engineeringpolynomialpreprocessing5 tests
- Sparse matrix multiplicationMediumlinear-algebramatmulsparsealgorithms4 tests
- Sparse mixture-of-experts layerHardnumpymoetransformerroutingsparse5 tests
- Speculative decoding verificationMediumnumpyllminferencespeculative-decodingsampling5 tests
- Step LR schedulerEasyoptimizationlearning-rateschedulertraining4 tests
- SwiGLU activationMediumnumpyactivationtransformerllmglu4 tests
- Swish / SiLU activationEasynumpyactivationneural-netsiluswish4 tests
- TD(0) value updateEasynumpyreinforcement-learningtemporal-differencevalue-function5 tests
- Temperature scalingEasynumpydecodingsamplingfundamentals5 tests
- TF-IDFMediumnumpynlptext-featurestf-idfinformation-retrieval4 tests
- Time-series anomaly detectionMediumnumpytime-seriesanomaly-detectionrobust-statistics5 tests
- Top-k samplingEasynumpydecodingsampling6 tests
- Top-p (nucleus) samplingMediumsamplingdecodingnumpy6 tests
- Triplet lossMediumnumpylossmetric-learningembeddingstriplet4 tests
- UCB1 multi-armed banditEasynumpyreinforcement-learningbanditucbexploration5 tests
- Unigram probability from a corpusEasynlplanguage-modelsprobabilityfundamentals4 tests
- VAE ELBO lossMediumnumpyvaegenerativeelbolosskl-divergence5 tests
- Viterbi algorithmMediumnumpyhmmdynamic-programmingsequence-modelingdecoding4 tests
- Wasserstein distance (1-D)Mediumnumpyinformation-theorywassersteindistribution-distancestatistics5 tests
- Weighted cross-entropyEasynumpylossclassificationimbalancecross-entropy4 tests
- Weighted multinomial samplingEasynumpysamplingprobabilitymultinomial5 tests
| Title | Difficulty | Topics | Companies | Tests |
|---|---|---|---|---|
| Accuracy score | Easy | numpymetricsevaluationclassificationfundamentals | 4 | |
| AdaBoost fit (decision stumps) | Medium | numpyensembleboostingadaboostclassical-mlclassification | 4 | |
| Adadelta optimizer | Medium | numpyoptimizationoptimizeradaptiveadadelta | 4 | |
| Adagrad optimizer | Easy | numpyoptimizationoptimizeradaptiveadagrad | 4 | |
| Adam optimiser step | Medium | numpyoptimizationadam | 6 | |
| Adamax optimizer | Medium | numpyoptimizationoptimizeradaptiveadamax | 4 | |
| AdamW step (decoupled weight decay) | Medium | numpyoptimizationadamwweight-decay | 5 | |
| Affine coupling layer (normalizing flow) | Medium | numpynormalizing-flowgenerativerealnvpinvertible | 5 | |
| Autocorrelation function | Medium | numpytime-seriesautocorrelationstatistics | 5 | |
| Autoencoder forward pass | Easy | numpyautoencoderneural-networkrepresentation-learning | 4 | |
| Bag-of-words encoding | Easy | numpynlptext-featuresbag-of-words | 4 | |
| Basic autograd operations | Medium | autogradbackpropagationneural-networkcomputational-graph | 4 | |
| BatchNorm forward (train + eval modes) | Medium | numpynormalizationbatch-normregularization | 5 | |
| Beam search decoding | Medium | numpydecodingbeam-searchllmsequence | 5 | |
| Bellman equation for value iteration | Medium | numpyreinforcement-learningvalue-iterationbellmanmdp | 5 | |
| Bernoulli Naive Bayes classifier | Medium | numpynaive-bayesclassical-mlclassificationprobability | 4 | |
| Best Gini-based split (decision tree) | Medium | numpydecision-treeclassical-mlginiclassification | 4 | |
| Binary classification with logistic regression | Easy | numpyclassificationlogistic-regressioninferencedecision-boundary | 4 | |
| BLEU score (unigram) | Medium | nlpmetricsmachine-translationevaluationbleu | 4 | |
| BPE: apply merges | Medium | tokenizationBPEalgorithms | 8 | |
| Train BPE merges from a corpus | Hard | pythontokenizationbpe | 6 | |
| Conv2D forward (naive, stride 1, no pad) | Medium | numpycnnconvolutionbuild-cnn | 5 | |
| Conv2D forward (padding + stride) | Medium | numpycnnconvolutionbuild-cnn | 5 | |
| Max pooling 2D forward | Easy | numpycnnpoolingbuild-cnn | 6 | |
| Average pooling 2D forward | Easy | numpycnnpoolingbuild-cnn | 6 | |
| Conv2D backward (dx, dW) | Hard | numpycnnbackpropbuild-cnn | 5 | |
| Mini-CNN forward (capstone) | Medium | numpycnnclassificationcapstonebuild-cnn | 5 | |
| Token embedding lookup | Easy | numpytransformerembeddingsbuild-gpt | 5 | |
| Sinusoidal positional encoding | Easy | numpytransformerpositional-encodingbuild-gpt | 6 | |
| Scaled dot-product self-attention | Medium | numpytransformerattentionbuild-gpt | 6 | |
| Causal mask: build + apply | Easy | numpytransformermaskingbuild-gpt | 5 | |
| Multi-head split + combine | Medium | numpytransformerreshapebuild-gpt | 6 | |
| Multi-Head Attention (full layer) | Medium | numpytransformermulti-head-attentionbuild-gpt | 5 | |
| LayerNorm forward | Easy | numpytransformernormalizationbuild-gpt | 5 | |
| Transformer block forward (pre-LN, residual) | Hard | numpytransformercapstonebuild-gpt | 5 | |
| Linear forward (Wx + b) | Easy | numpyneural-netfundamentalsbuild-nn | 5 | |
| ReLU forward + backward | Easy | numpyneural-netactivationbackpropbuild-nn | 5 | |
| Sigmoid forward + backward | Easy | numpyneural-netactivationbackpropbuild-nn | 6 | |
| MSE loss + backward | Easy | numpyneural-netlossbackpropbuild-nn | 6 | |
| Linear backward (chain rule) | Medium | numpyneural-netbackpropcalculusbuild-nn | 6 | |
| SGD step (in place) | Easy | numpyneural-netoptimizationsgdbuild-nn | 6 | |
| Train a 2-layer MLP on XOR | Hard | numpyneural-nettraining-loopcapstonebuild-nn | 5 | |
| Byte-level UTF-8 tokenizer | Medium | pythontokenizationunicode | 6 | |
| Cohen's kappa score | Medium | numpymetricsevaluationagreementstatistics | 4 | |
| Confusion matrix | Easy | numpymetricsevaluationclassification | 4 | |
| Contrastive loss | Medium | numpylossmetric-learningembeddingssiamese | 4 | |
| Warmup + cosine decay LR schedule | Easy | numpytraininglr-scheduleoptimization | 6 | |
| Covariance matrix | Easy | numpylinear-algebrastatisticscovariance | 4 | |
| Cross-entropy gradient | Medium | calculusbackpropnumpyfundamentals | 6 | |
| DBSCAN clustering | Medium | numpyclusteringclassical-mldensity-basedunsupervised | 4 | |
| Decision tree learning (ID3) | Hard | decision-treeclassical-mlentropyinformation-gainclassification | 5 | |
| Dense block with 2D convolutions | Medium | numpycnndensenetconvolutionneural-network | 5 | |
| Dice loss | Easy | numpylosssegmentationcomputer-visiondice | 4 | |
| Diffusion forward process | Medium | numpydiffusiongenerativeddpm | 5 | |
| Divide dataset by feature threshold | Easy | numpydecision-treeclassical-mldata-splitting | 4 | |
| DPO loss | Medium | numpyllmalignmentdpopreference-learningloss | 5 | |
| Dropout layer (forward & backward) | Medium | numpyregularizationdropoutneural-networkbackpropagation | 6 | |
| Dynamic-Tanh (DyT) | Medium | numpyactivationtransformernormalization-freedyt | 4 | |
| Early stopping on validation loss | Easy | trainingregularizationearly-stoppingmodel-evaluation | 4 | |
| Efficient sparse window attention | Medium | numpyattentiontransformersparselong-context | 5 | |
| Elastic-Net regression (gradient descent) | Medium | numpyregressionregularizationlassoridgegradient-descent | 4 | |
| Euclidean distance matrix | Easy | numpylinear-algebradistancesvectorization | 4 | |
| Exponential moving average of weights | Easy | numpytrainingemaweight-averagingregularization | 5 | |
| Exponential LR scheduler | Easy | optimizationlearning-rateschedulertraining | 4 | |
| F1 score (binary classification) | Easy | numpymetricsevaluationfundamentals | 6 | |
| Fisher-Yates shuffle | Easy | numpysamplingshufflepermutationalgorithm | 5 | |
| FlashAttention tiled forward | Hard | numpyattentiontransformeronline-softmaxflash-attention | 6 | |
| Focal loss (multiclass) | Medium | numpylossimbalanceclassification | 6 | |
| Gaussian process regression (RBF) | Hard | numpygaussian-processregressionkernel-methodsbayesian | 4 | |
| GCN layer (message passing) | Medium | numpygraph-neural-networkgcnmessage-passing | 5 | |
| GELU backward (tanh approximation) | Medium | numpyactivationbackpropcalculusgelu | 6 | |
| GELU forward (tanh approximation) | Easy | numpyactivationtransformergelu | 6 | |
| Generate random subsets (bootstrap) | Easy | numpysamplingbootstrapbaggingresampling | 5 | |
| GMM E-step (responsibilities) | Medium | numpyclusteringgmmemunsupervisedprobability | 4 | |
| Gradient checkpointing forward | Easy | numpytrainingmemory-optimizationgradient-checkpointing | 4 | |
| Clip gradients by global L2 norm | Easy | numpytrainingstabilityoptimization | 6 | |
| Graph Laplacian | Medium | numpygraphlinear-algebraspectrallaplacian | 4 | |
| Greedy decoding loop | Easy | numpydecodingautoregressive | 5 | |
| Group normalization | Medium | numpynormalizationdeep-learningcnngroup-norm | 4 | |
| Grouped-query attention | Medium | numpyattentiontransformergqainference | 5 | |
| GRPO objective | Hard | numpyreinforcement-learninggrporlhfllm | 5 | |
| He weight initialization | Easy | numpyinitializationneural-networktrainingrelu | 4 | |
| Huber & Hinge losses | Easy | numpylossregressionsvmrobust | 4 | |
| Instance normalization | Medium | numpynormalizationcomputer-visionstyle-transferinstance-norm | 4 | |
| Inverse-transform sampling | Easy | numpysamplingprobabilitymonte-carlo | 5 | |
| IoU of bounding boxes | Easy | computer-visionobject-detectionmetriciou | 5 | |
| Jaccard similarity | Easy | numpymetricssimilaritysetsiou | 4 | |
| k-fold cross-validation | Easy | numpymodel-evaluationcross-validationresampling | 5 | |
| KL divergence (discrete) | Easy | numpylossinformation-theoryfundamentals | 6 | |
| K-means: one iteration | Medium | numpyclassical-mlclustering | 6 | |
| k-NN classification (majority vote) | Easy | numpyclassical-mlclassificationfundamentals | 6 | |
| KV cache for autoregressive inference | Medium | numpyattentiontransformerinferencekv-cache | 4 | |
| KV cache compression (MLA) | Hard | numpyattentiontransformerinferencemlakv-cache | 4 | |
| Label-smoothed cross-entropy | Medium | numpylossregularizationclassification | 5 | |
| Leaky ReLU activation | Easy | numpyactivationneural-netrelu | 4 | |
| Learning-rate range test | Medium | numpytraininglearning-ratehyperparameter-tuning | 4 | |
| Linear regression — gradient descent | Easy | numpyregressionlinear-regressiongradient-descentoptimization | 4 | |
| Linear regression — normal equation | Medium | numpyregressionlinear-regressionclassical-mlleast-squares | 3 | |
| Lion optimizer step | Medium | numpyoptimizationoptimizerlionsign-momentum | 4 | |
| Log-softmax | Easy | numpysoftmaxnumerical-stabilitylossfundamentals | 5 | |
| Logistic regression — gradient descent | Medium | numpyregressionlogistic-regressionclassificationgradient-descent | 4 | |
| LoRA forward pass | Easy | numpylorafine-tuningpeftllm | 5 | |
| Matmul backward | Medium | numpybackpropmatmulcalculus | 5 | |
| Mean reciprocal rank (MRR) | Easy | rankingmetricsretrievalevaluation | 4 | |
| METEOR score | Medium | nlpmetricsmachine-translationevaluationmeteor | 4 | |
| Multi-class cross-entropy loss | Easy | numpylossclassificationcross-entropyfundamentals | 4 | |
| Mutual information | Medium | numpyinformation-theorymutual-informationfeature-selectionstatistics | 5 | |
| Nesterov accelerated gradient | Medium | numpyoptimizationoptimizermomentumnesterov | 4 | |
| Neural ODE forward Euler | Medium | numpyneural-odenumerical-integrationeulergenerative | 5 | |
| Noisy top-k gating | Medium | numpymoegatingroutingtransformer | 5 | |
| Orthonormal basis (Gram-Schmidt) | Medium | numpylinear-algebragram-schmidtorthogonalization | 4 | |
| PageRank (power iteration) | Medium | numpygraphpagerankpower-iterationlinear-algebra | 4 | |
| Pairwise cosine-similarity matrix | Easy | numpyembeddingssimilarityretrievalcosine | 4 | |
| Pearson correlation coefficient | Easy | numpystatisticscorrelationfundamentals | 4 | |
| Pegasos kernel SVM | Medium | numpysvmkernel-methodsclassical-mlclassification | 4 | |
| Perplexity from log-probs | Easy | numpynlplanguage-modelsmetricsevaluation | 4 | |
| Pointwise mutual information (PMI) | Medium | numpynlpinformation-theorystatisticspmi | 4 | |
| Policy gradient with REINFORCE | Medium | numpyreinforcement-learningpolicy-gradientreinforce | 5 | |
| PPO clipped objective | Medium | numpyreinforcement-learningpporlhfpolicy-gradient | 5 | |
| Precision@k and NDCG@k | Medium | numpyrankingmetricsretrievalndcg | 4 | |
| Precision metric | Easy | numpymetricsevaluationclassificationfundamentals | 4 | |
| Principal Component Analysis (PCA) | Medium | numpypcadimensionality-reductionlinear-algebraunsupervised | 4 | |
| Prioritized experience replay | Medium | numpyreinforcement-learningexperience-replaydqnimportance-sampling | 5 | |
| Q-learning for MDPs | Medium | numpyreinforcement-learningq-learningmdpvalue-based | 4 | |
| Rejection sampling | Medium | numpysamplingmonte-carloprobability | 5 | |
| Repetition penalty (HuggingFace-style) | Medium | numpydecodingsampling | 6 | |
| Reservoir sampling | Medium | numpysamplingstreamingalgorithm | 5 | |
| Residual block with shortcut | Easy | numpyneural-networkresnetresidualcnn | 4 | |
| Ridge regression loss | Easy | numpyregressionregularizationridgeloss | 4 | |
| RMSNorm | Easy | numpynormalizationtransformerllmrmsnorm | 4 | |
| RMSprop optimizer | Easy | numpyoptimizationoptimizeradaptivermsprop | 4 | |
| Vanilla RNN cell forward | Medium | numpyrnnsequence-models | 5 | |
| ROC-AUC from scratch | Medium | numpymetricsevaluationclassificationroc-auc | 4 | |
| Rotary Position Embedding (RoPE) | Hard | numpytransformerpositional-encodingrope | 6 | |
| ROUGE-1 score | Easy | nlpmetricssummarizationevaluationrouge | 4 | |
| Scaled dot-product attention | Medium | numpytransformersattention | 5 | |
| SELU activation | Easy | numpyactivationneural-netself-normalizingselu | 4 | |
| Sequence padding & masking | Easy | numpynlpsequencepreprocessingmasking | 5 | |
| SGD with momentum (one step) | Easy | numpyoptimizationsgdmomentum | 5 | |
| Shannon entropy | Easy | numpyinformation-theoryentropystatistics | 5 | |
| Singular Value Decomposition (2x2) | Hard | numpylinear-algebrasvdmatrix-factorization | 4 | |
| Softmax backward | Medium | numpybackpropsoftmaxcalculus | 5 | |
| Softmax from scratch | Easy | numpyfundamentalsnumerical-stability | 6 | |
| Softmax (multinomial) regression | Medium | numpyregressionclassificationsoftmaxmulticlassgradient-descent | 4 | |
| Sorted polynomial features | Medium | numpyfeature-engineeringpolynomialpreprocessing | 5 | |
| Sparse matrix multiplication | Medium | linear-algebramatmulsparsealgorithms | 4 | |
| Sparse mixture-of-experts layer | Hard | numpymoetransformerroutingsparse | 5 | |
| Speculative decoding verification | Medium | numpyllminferencespeculative-decodingsampling | 5 | |
| Step LR scheduler | Easy | optimizationlearning-rateschedulertraining | 4 | |
| SwiGLU activation | Medium | numpyactivationtransformerllmglu | 4 | |
| Swish / SiLU activation | Easy | numpyactivationneural-netsiluswish | 4 | |
| TD(0) value update | Easy | numpyreinforcement-learningtemporal-differencevalue-function | 5 | |
| Temperature scaling | Easy | numpydecodingsamplingfundamentals | 5 | |
| TF-IDF | Medium | numpynlptext-featurestf-idfinformation-retrieval | 4 | |
| Time-series anomaly detection | Medium | numpytime-seriesanomaly-detectionrobust-statistics | 5 | |
| Top-k sampling | Easy | numpydecodingsampling | 6 | |
| Top-p (nucleus) sampling | Medium | samplingdecodingnumpy | 6 | |
| Triplet loss | Medium | numpylossmetric-learningembeddingstriplet | 4 | |
| UCB1 multi-armed bandit | Easy | numpyreinforcement-learningbanditucbexploration | 5 | |
| Unigram probability from a corpus | Easy | nlplanguage-modelsprobabilityfundamentals | 4 | |
| VAE ELBO loss | Medium | numpyvaegenerativeelbolosskl-divergence | 5 | |
| Viterbi algorithm | Medium | numpyhmmdynamic-programmingsequence-modelingdecoding | 4 | |
| Wasserstein distance (1-D) | Medium | numpyinformation-theorywassersteindistribution-distancestatistics | 5 | |
| Weighted cross-entropy | Easy | numpylossclassificationimbalancecross-entropy | 4 | |
| Weighted multinomial sampling | Easy | numpysamplingprobabilitymultinomial | 5 |
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