Deep Learning Fundamentals
Deep learning is a subfield of machine learning that automatically learns hierarchical representations of data through multi-layer neural networks. This section covers the fundamental concepts and core components of deep learning.
Contents:
- Introduction to Deep Learning — History, universal approximation theorem, deep learning frameworks
- Feedforward Neural Networks — MLP architecture, activation functions, backpropagation
- Probability & Statistics in DL — Maximum likelihood estimation, Bayesian inference
- Loss Functions — Cross-entropy, MSE, contrastive loss
- Interview Notes — Summary of deep learning core concepts