jcorpac 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.