ML Deep Learning Foundations

Deep learning powers complex vision and sequence tasks.

Core Components

• Architectures: CNNs (Vision), RNNs/LSTMs (Sequences), Transformers (Everything).
• Optimizers: Adam, SGD, RMSprop.
• Loss Functions: CrossEntropy (Classification), MSE (Regression).

Advanced Patterns

• Transfer Learning: Fine-tuning pre-trained models (e.g., ResNet, BERT).
• Regularization: Dropout, Batch Normalization, and Weight Decay.
• Early Stopping: Preventing overfitting by monitoring validation loss.

Frameworks

• PyTorch: Researcher favorite, highly dynamic.
• TensorFlow / Keras: Mature ecosystem, production-ready.