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NVIDIA Interview Problems (21)
Problems reported from NVIDIA data science interviews
| Status | Title | Difficulty | |||
|---|---|---|---|---|---|
| GPU Training Basics Pro | Easy | ||||
| Adam Optimizer Internals Pro | Medium | ||||
| Batch Normalization Mechanism Pro | Medium | ||||
| Convolution Stride and Output Size Pro | Medium | ||||
| Distributed Training: DDP Pro | Medium | ||||
| Unlock all 21+ problems | |||||
| Freezing Layers in Transfer Learning Pro | Medium | ||||
| He Initialization for ReLU Pro | Medium | ||||
| Mixed Precision Training Pro | Medium | ||||
| Model Distillation Pro | Medium | ||||
| ONNX Format Pro | Medium | ||||
| Unlock all 21+ problems | |||||
| Quantization for LLM Inference Pro | Medium | ||||
| Skip Connections in ResNets Pro | Medium | ||||
| SVM Kernel Selection Pro | Medium | ||||
| AdaGrad Limitations Pro | Hard | ||||
| Cosine Annealing Schedule Pro | Hard | ||||
| Unlock all 21+ problems | |||||
| Flash Attention Pro | Hard | ||||
| GAN Training Dynamics Pro | Hard | ||||
| KV Cache in Autoregressive Generation Pro | Hard | ||||
| Neural Network Forward Pass Pro | Hard | ||||
| Speculative Decoding Pro | Hard | ||||
| Unlock all 21+ problems | |||||
| TF-IDF Calculator Pro | Hard | ||||