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Deep Learning Fundamentals

Neural networks, backpropagation, activation functions, optimization.

Course Duration: 10 hours​

What You'll Learn​

  • Perceptrons and neural networks
  • Activation functions
  • Forward and backward propagation
  • Gradient descent variants
  • Regularization techniques
  • Building neural networks from scratch

Prerequisites​

  • Mathematics for AI
  • Python programming

Course Modules​

  1. Introduction to Deep Learning
  2. Perceptrons
  3. Multi-Layer Perceptrons
  4. Activation Functions
  5. Forward Propagation
  6. Loss Functions
  7. Backpropagation
  8. Gradient Descent Variants
  9. Regularization (Dropout, L1/L2)
  10. Batch Normalization
  11. Weight Initialization
  12. Building a Neural Network from Scratch

Key Concepts​

  • Neurons and layers
  • Weights and biases
  • Chain rule
  • Vanishing/exploding gradients