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Neural Network: Feedforward and Backpropagation with NumPy
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Understanding the inner workings of Artificial Neural Network (NN) is crucial for anyone delving into the field of deep learning. This post will explore forward propagation, backward propagation, the critical role of weights and biases, and positive vs negative gradients.
Using Python libraries like PyTorch or TensorFlow makes building a neural network architecture seem straightforward - until you want to really understand how each neuron contributes to the learning process.
Gaining this deep intuition can be challenging, especially as the network grows in size (trust me, it can get overwhelming even with the simplest!). To make it easier to grasp, I’ll break things down step by step with underlying matrices. And yes we will only use NumPy library !
Understanding Neural Network Layer Interactions
The core concept behind the large-scale processing of data in a Neural Network (NN) is based on the principle that each layer’s neurons utilize the activations (outputs) from the preceding layer as their inputs.
