Designing neurons from scratch without using any libraries
In this assignment, I implement the gradient descent and backpropagation mathematically from scratch, and implement it in Python code. These form as the building blocks of the abstract neural network layers, using which I explore how models learn patterns within data.
I also test the effects of varying the following structural and algorithmic hyperparameters on the learning process
- Loss / Cost Function
- Activation Function
- Optimization algorithm
- Learning Iterations / Epochs
- Learning Rate
- Regularization
- Train-Test Splits
- Number of hidden units
- Initialization
Link to Notebook
