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How I created my own Neural Network from scratch (and how you can too)!
- In this article, a Feed-Forward Neural Network (FFN) is built using only numpy to identify handwritten digits from the MNIST dataset.
- Prior proficiency in Python is assumed, with a recommendation to refer to 3Blue1Brown’s videos on neural networks for beginners.
- The creation involves understanding neuron functions, weights, biases, and implementing them in code step by step.
- Introduction of non-linearity using activation functions like ReLU for better neural network performance and learning.
- Exploration of loss functions such as cross-entropy to evaluate model performance and feedback for improvement.
- Backpropagation process involves computing gradients, updating weights and biases using derivatives and softmax functions.
- Training the network involves dividing data into batches for efficiency and avoiding issues like exploding gradients.
- Testing and adjusting the epochs to balance between accuracy and overfitting to fine-tune the neural network model.
- The article concludes with successfully building a neural network achieving 92.45% accuracy in identifying handwritten digits.
- Readers are encouraged to experiment with parameters, test on different datasets, and further explore deep learning topics for continuous learning.
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