Building a Simple Neural Network From Scratch in Python Using Backpropagation

1. 1Import necessary Python libraries like NumPy
2. 2Define the neural network architecture with input, hidden, and output layers
3. 3Implement the forward pass to calculate output
4. 4Apply backpropagation to calculate errors and update weights
5. 5Test the neural network with sample data to evaluate its performance

Data scientists and AI engineers can benefit from this tutorial to improve their understanding of neural networks and implement them in their projects

💡 Backpropagation is a crucial algorithm for training neural networks by minimizing errors and updating weights

🤖 Build a simple neural network from scratch in Python using backpropagation! 💻