Welcome to the Fundamentals of Deep Learning

Chapters:

• Introduction
• Difference Between AI, ML and DL
• Biological Inspiration
• Basics of Neural Network
  • Perceptron
  • Neuron
  • Perceptron vs Neuron
  • Input Layer
  • Weight
  • Hidden Layer
  • Output Layer
  • Activation Functions
  • Forward Propagation
  • Loss Function
  • Back Propagation
  • Gradient Descent
  • Optimizers
  • Multilayer Perceptron
  • Overfitting and Underfitting
  • Techniques to Combat Overfitting
  • Matrix Evaluation