Convolutional Neural Networks Questions

Deep and practical expertise in convolutional neural networks and their architectures for spatial and visual data tasks. Candidates should understand convolution operations including filters and kernels, feature maps and receptive field, stride and padding, pooling operations, activation functions and normalization methods, and how these components combine to form a hierarchical representation of spatial features. Know architectural building blocks such as residual connections, dense connections, bottleneck layers, inception modules, squeeze and excitation blocks, depthwise separable and grouped convolutions, dilated convolutions, and attention augmented convolutions. Be familiar with canonical model families and examples such as AlexNet, VGG, ResNet, Inception, DenseNet, MobileNet, EfficientNet and modern hybrids that combine convolutional features with transformer based components, and be able to reason about design trade offs between depth and width, representational capacity, parameter counts, computational cost, latency and memory footprint. Understand training and regularization strategies including batch normalization and layer normalization, dropout, weight decay, data augmentation, optimizers and learning rate schedules, initialization, and techniques for training from scratch versus fine tuning pretrained models. Know transfer learning and domain adaptation approaches, and model compression and deployment techniques such as pruning, quantization, knowledge distillation and low rank factorization for mobile and edge systems. Be able to select and adapt architecture choices for specific vision tasks such as classification, object detection and instance segmentation, and to evaluate models using appropriate metrics such as classification accuracy and top k accuracy, intersection over union and mean average precision on standard benchmarks such as ImageNet and Common Objects in Context.