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Neural Network Implementation in Python

This project focuses on implementing a neural network from scratch in Python. The steps include data preprocessing, defining the network architecture, initializing weights, performing forward propagation, calculating loss, backpropagation, updating w...


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Prompt

How can I implement a neural network from scratch in Python?

Answer

To implement a neural network from scratch in Python, you can follow these steps:

  1. Data Preprocessing: Normalize the input features and one-hot encode the output labels.

  2. Network Architecture: Define the number of layers, number of neurons per layer, and activation function for each layer.

  3. Initialize Weights: Randomly initialize the weights and biases for each layer.

  4. Forward Propagation: Perform matrix multiplication between the input and weights, apply the activation function, and propagate the output to the next layer.

  5. Loss Calculation: Calculate the loss between the predicted output and the actual output.

  6. Backpropagation: Calculate the gradient of the loss with respect to the weights and biases using the chain rule.

  7. Update Weights: Update the weights and biases using gradient descent.

  8. Repeat Steps 4-7: Repeat steps 4-7 for a defined number of iterations or until the loss converges.

  9. Prediction: Use the trained network to make predictions on new data.

The code should include necessary imports, documentation blocks, input validation, and inline comments explaining the logic behind each step.

Example scenario: Implement a neural network to classify handwritten digits from the MNIST dataset. Preprocess the data, define a network with three layers (input, hidden, and output), initialize the weights, perform forward propagation, calculate loss, backpropagate the gradients, update the weights, and repeat these steps for a certain number of epochs. Finally, use the trained network to predict the digit labels for new handwritten images.

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Description

This project aims to implement a neural network from scratch in Python, allowing users to gain a better understanding of the inner workings of neural networks. The steps involved in this implementation include data preprocessing, network architecture definition, weight initialization, forward propagation, loss calculation, backpropagation, weight updating, and prediction making.

Firstly, the input data will be preprocessed, including normalizing the input features and one-hot encoding the output labels. Then, the network architecture will be defined, specifying the number of layers, neurons per layer, and activation function for each layer.

Next, the weights and biases for each layer will be randomly initialized. The forward propagation step will involve performing matrix multiplication between the input and weights, applying the activation function, and propating the output to the next layer.

The loss will be calculated by comparing the predicted output with the actual output. Backpropagation will then be performed to calculate the gradient of the loss with respect to the weights and biases using the chain rule.

The weights and biases will be updated using the gradient descent algorithm. These steps will be repeated for a specified number of iterations or until the loss converges.

Finally, the trained network will be used to make predictions on new data.

An example scenario is provided where the neural network is implemented to classify handwritten digits from the MNIST dataset. The data will be preprocessed, the network architecture will be defined with an input layer, a hidden layer, and an output layer, weights will be initialized, and the forward propagation, loss calculation, backpropagation, and weight updating steps will be performed for a specified number of epochs.

By following the code provided, users will be able to gain a deeper understanding of how neural networks work and how to implement them from scratch in Python.

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