Deep Learning

Build neural networks from scratch, understanding perceptrons, activation functions, forward propagation, and the mathematical foundations of deep learning.

Master backpropagation algorithm, gradient descent variants (SGD, Adam, RMSprop), learning rate scheduling, and optimization techniques.

Explore activation functions (ReLU, Leaky ReLU, ELU, Swish), weight initialization strategies (Xavier, He), and vanishing/exploding gradients.

Master PyTorch tensors, autograd, building models with nn.Module, training loops, and GPU acceleration for deep learning.

Learn TensorFlow 2.x, Keras API, Sequential and Functional models, custom layers, callbacks, and TensorBoard for visualization.

Compare PyTorch vs TensorFlow, understand framework strengths, choose the right tool for your project, and migration strategies.

Master convolutional layers, pooling, feature maps, CNN design principles, and building custom CNN architectures for computer vision.

Implement ResNet with skip connections, Inception modules, EfficientNet scaling, and modern CNN architectures for state-of-the-art performance.

Apply transfer learning with pre-trained models, fine-tuning strategies, feature extraction, and domain adaptation for computer vision.

Build recurrent neural networks, understand LSTM architecture, handle sequence data, and solve vanishing gradient problems in RNNs.

Implement GRU cells, bidirectional RNNs, sequence-to-sequence models, and advanced architectures for sequential data.

Master Transformer architecture, self-attention mechanism, multi-head attention, positional encoding, and the foundation of modern NLP.

Implement BERT for understanding tasks, GPT for generation, use Hugging Face Transformers, and apply pre-trained language models.

Master batch normalization, layer normalization, gradient clipping, mixed precision training, and distributed training strategies.

Implement dropout, L1/L2 regularization, early stopping, data augmentation, and strategies to prevent overfitting in deep learning.

Export models (ONNX, TorchScript), optimize for inference, deploy with FastAPI, containerize with Docker, and scale in production.