Neural Networks For Your Dog - 3.3 Gradient Descent

3.3 Gradient Descent

In this lecture, we’ll see how neural networks learn optimal weights via gradient descent (commonly called “backpropagation. Then we’ll build a neural network with logistic activation functions and log loss objective function.

Code

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