Understand the fundamentals of neural networks by building a simple feedforward neural network from scratch. This will involve learning about key concepts such as backpropagation, gradient descent, and activation functions.
Implement a simple feedforward neural network using only Python and NumPy.
Backpropagation, gradient descent, activation functions.
Pure Python, NumPy.
- Learn the structure of neural networks, which includes layers of neurons (input layer, hidden layers, and output layer).
- Understand the roles of weights and biases in the network.
- Forward Pass: Calculate the output of the neural network by propagating inputs through the network.
- Activation Functions: Introduce non-linearity using functions like sigmoid, tanh, and ReLU.
- Loss Function: Measure the difference between the predicted output and the actual output using functions like Mean Squared Error (MSE) or Cross-Entropy Loss.
- Backward Pass (Backpropagation): Update the weights and biases based on the gradient of the loss function.
- Gradient Descent: Minimize the loss function by iteratively updating the weights and biases.
- Initialize the Network:
- Define the architecture (number of layers, number of neurons per layer) and initialize weights and biases.
- Define Activation Functions:
- Implement common activation functions and their derivatives.
- Forward Pass:
- Implement the process of calculating the network's output.
- Compute Loss:
- Implement the loss function to evaluate the network's performance.
- Backward Pass (Backpropagation):
- Calculate the gradient of the loss function with respect to each weight and bias, and update them accordingly.
- Training Loop:
- Create a loop to iteratively perform the forward pass, compute loss, perform backpropagation, and update weights. Train the network on a sample dataset.
- Experiment with different activation functions, learning rates, and network architectures.
- Observe how changes affect the network's performance and learning rate.