#cnn

Design and implement deep learning architectures for various applications with optimization and regularization techniques. Neural network fundamentals: 1. Architecture design: input layer sizing, hidden layers (2-5 for most tasks), output layer activation functions. 2. Activation functions: ReLU for hidden layers, sigmoid/softmax for output, leaky ReLU for gradient problems. 3. Weight initialization: Xavier/Glorot for sigmoid/tanh, He initialization for ReLU networks. Convolutional Neural Networks (CNNs): 1. Architecture patterns: LeNet (digit recognition), AlexNet (ImageNet), ResNet (skip connections), EfficientNet (compound scaling). 2. Layer design: Conv2D (3x3 filters standard), MaxPooling (2x2), dropout (0.2-0.5), batch normalization. 3. Transfer learning: pre-trained models (ImageNet), fine-tuning last layers, feature extraction vs. full training. Recurrent Neural Networks (RNNs): 1. LSTM/GRU: sequential data processing, vanishing gradient solution, bidirectional architectures. 2. Attention mechanisms: self-attention, multi-head attention, transformer architecture. Regularization techniques: 1. Dropout: 20-50% during training, prevents overfitting, Monte Carlo dropout for uncertainty. 2. Batch normalization: normalize layer inputs, accelerated training, internal covariate shift reduction. 3. Early stopping: monitor validation loss, patience 10-20 epochs, save best model weights. Training optimization: Adam optimizer (lr=0.001), learning rate scheduling, gradient clipping for RNNs, mixed precision training for efficiency.

Implement computer vision solutions using deep learning for image classification, object detection, and visual analysis. Image preprocessing: 1. Data augmentation: rotation (±15°), horizontal flip, zoom (0.8-1.2x), brightness adjustment. 2. Normalization: pixel values [0,1], ImageNet normalization (mean=[0.485,0.456,0.406), std=[0.229,0.224,0.225]). 3. Resizing strategies: maintain aspect ratio, center cropping, padding to target size. Classification architectures: 1. ResNet: skip connections, deeper networks (50-152 layers), batch normalization. 2. EfficientNet: compound scaling, mobile-optimized, state-of-the-art accuracy/efficiency trade-off. 3. Vision Transformer (ViT): attention-based, patch embedding, competitive with CNNs. Object detection: 1. YOLO (You Only Look Once): real-time detection, single-stage detector, anchor boxes. 2. R-CNN family: two-stage detection, region proposals, high accuracy applications. 3. SSD (Single Shot Detector): multi-scale feature maps, speed/accuracy balance. Semantic segmentation: 1. U-Net: encoder-decoder, skip connections, medical imaging applications. 2. DeepLab: atrous convolution, conditional random fields, accurate boundary detection. Transfer learning: 1. ImageNet pre-training: feature extraction (freeze early layers), fine-tuning (unfreeze gradually). 2. Domain adaptation: medical images, satellite imagery, artistic style transfer. Evaluation metrics: top-1 accuracy (>90% excellent), mAP for detection (>0.5), IoU for segmentation (>0.7), inference time (<50ms for real-time applications).