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deep learning premium interviews

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attention training gradient neural network fundamentals backpropagation gradient dynamics computer vision architectures sequential models lstm transformer architecture attention mechanisms normalization techniques regularization techniques optimization algorithms loss functions transfer learning

dl-interview-premium(20)

Neural Networks: Forward/Backward Propagation, Activation Functions — Asked at Google & OpenAI

Deep Learning Premium InterviewsPremium

Backpropagation: Chain Rule, Gradient Flow, Vanishing Gradients — Asked at DeepMind & Anthropic

Deep Learning Premium InterviewsPremium

Convolutional Neural Networks: Filters, Pooling, Architectures — Asked at NVIDIA & Meta

Deep Learning Premium InterviewsPremium

RNN & LSTM: Sequential Data, Gate Mechanisms, Attention — Asked at Google & Amazon

Deep Learning Premium InterviewsPremium

Transformers: Self-Attention, Multi-Head Attention, Positional Encoding — Asked at OpenAI & Google

Deep Learning Premium InterviewsPremium

Attention Mechanism: Scaled Dot-Product, Cross-Attention, Flash Attention — Asked at OpenAI & Anthropic

Deep Learning Premium InterviewsPremium

Batch Normalization: Internal Covariate Shift, Layer Norm, Group Norm — Asked at NVIDIA & Meta

Deep Learning Premium InterviewsPremium

Dropout: Regularization, Inverted Dropout, Monte Carlo Dropout — Asked at Google & Amazon

Deep Learning Premium InterviewsPremium

Optimizers: SGD, Adam, AdamW, Learning Rate Schedules — Asked at OpenAI & DeepMind

Deep Learning Premium InterviewsPremium

Loss Functions: Cross-Entropy, Focal Loss, Contrastive Loss — Asked at Meta & Google

Deep Learning Premium InterviewsPremium

Transfer Learning: Fine-Tuning, Feature Extraction, Domain Adaptation — Asked at NVIDIA & Amazon

Deep Learning Premium InterviewsPremium

GANs: Generator, Discriminator, Training Stability — Asked at NVIDIA & Meta

Deep Learning Premium InterviewsPremium

Autoencoders: VAE, Denoising, Representation Learning — Asked at Google & OpenAI

Deep Learning Premium InterviewsPremium

ResNet: Skip Connections, Deep Networks, Residual Learning — Asked at NVIDIA & Microsoft

Deep Learning Premium InterviewsPremium

Object Detection: YOLO, Faster R-CNN, Anchor Boxes — Asked at Tesla & NVIDIA

Deep Learning Premium InterviewsPremium

Semantic Segmentation: U-Net, Mask R-CNN, Panoptic — Asked at Google & Meta

Deep Learning Premium InterviewsPremium

Reinforcement Learning: Q-Learning, Policy Gradient, PPO — Asked at DeepMind & OpenAI

Deep Learning Premium InterviewsPremium

Distributed Training: Data Parallelism, Model Parallelism, FSDP — Asked at NVIDIA & Google

Deep Learning Premium InterviewsPremium

Model Compression: Quantization, Pruning, Distillation — Asked at NVIDIA & Apple

Deep Learning Premium InterviewsPremium

Scaling Laws: Chinchilla, Compute-Optimal Training — Asked at OpenAI & Anthropic

Deep Learning Premium InterviewsPremium