Neural Networks For Your Dog - 2.6 Perceptron To Neural Network

2.6 Perceptron To Neural Network

In this lecture, we’ll discuss an intuitive way for interpreting why perceptrons work and how they can be used to mimic simple logical programs. However, we’ll also observe that perceptrons fail to solve the XOR problem, and how that deficiency (plus some knowledge about the human brain) leads to a natural derivation of multilayer perceptrons - AKA neural networks.

Course Curriculum

(See the code on GitHub)

  1. Introduction
    1.1 Introduction
  2. Perceptron
    2.1 MNIST Dataset
    2.2 Perceptron Model
    2.3 Perceptron Learning Algorithm
    2.4 Pocket Algorithm
    2.5 Multiclass Support
    2.6 Perceptron To Neural Network
  3. Neural Network
    3.1 Simple Images
    3.2 Random Weights
    3.3 Gradient Descent
    3.4 Multiclass Support
    3.5 Deep Learning
    3.6 Stochastic Gradient Descent
    3.7 Going Further

Additional Content

  1. Python NumPy For Your Grandma
  2. Python Pandas For Your Grandpa
  3. Introduction To Google Colab