Building a Neural Network Algorithm from Scratch

In this guide, we will learn how to build a neural network algorithm from scratch using Python. We will start with the basics of neural networks and gradually move towards building a fully functional neural network algorithm.

What is a Neural Network? A neural network is a machine learning model that is inspired by the human brain. It is a network of interconnected nodes, also known as artificial neurons, which work together to solve a problem.

The basic unit of a neural network is a neuron, which takes one or more inputs, processes them, and produces an output. The inputs and outputs are usually numerical values, and the processing is done using a mathematical function.

A neural network consists of multiple layers of neurons, where each layer processes the output of the previous layer. The first layer is called the input layer, and the last layer is called the output layer. The layers between the input and output layers are called hidden layers.

Steps to Build a Neural Network Algorithm To build a neural network algorithm from scratch, we will follow these steps:

1. Initialize the weights and biases
2. Implement the feedforward function
3. Implement the activation function
4. Implement the backpropagation function
5. Train the neural network
6. Make predictions using the trained model

1. Initialize the Weights and Biases

The weights and biases are the parameters that the neural network learns during training. Weights are the connections between neurons, and biases are the values added to the output of a neuron.

We initialize the weights randomly and the biases to zero. The number of weights and biases depends on the number of neurons in the previous and current layers.

2. Implement the Forward-propagation Function

The feedforward function takes the input and processes it through the layers to produce an output. We multiply the input by the weights, add the bias, and apply the activation function to get the output.

3. Implement the Activation Function

The activation function is applied to the output of a neuron to introduce non-linearity into the neural network. The most commonly used activation function is the sigmoid function, which maps the output to a value between 0 and 1.

4. Implement the Back-propagation Function

Backpropagation is the process of adjusting the weights and biases of the neural network to minimize the error between the predicted output and the actual output. We calculate the error by comparing the predicted output with the actual output and propagate it back through the network to adjust the weights and biases.

5. Train the Neural Network

Training the neural network involves repeatedly feeding the input data through the network, calculating the error, and adjusting the weights and biases using backpropagation. This process is repeated until the error is minimized, and the network can accurately predict the output for new input data.

6. Make Predictions Using the Trained Model

Once the neural network is trained, we can use it to make predictions on new input data. We feed the input data through the network, and the network produces the predicted output.

7. Let's Look at Each Step in Detail

In summary, building a neural network algorithm involves initializing the weights and biases, implementing the feedforward and activation functions, implementing the backpropagation function, training the neural network, and making predictions using the trained model. Each step plays a crucial role in building an accurate and efficient neural network. By understanding each step in detail, we can build a powerful neural network algorithm from scratch.

Created By: Maxwell Dalrymple