#deep-learning

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.