Understanding Neural Network Transformations and ReLU Activation #machinelearning #codemonarch #ai

Do you know how neural networks transform data? Let's break it down!

Consider a neural network with two input neurons, a hidden layer of three neurons, and two output neurons. We start with a 2D plane of uniformly distributed points and aim to map this from 2D to 3D.

Here’s how it works: We rotate the data into 3D space and apply a linear transformation using a weight matrix and bias.

Now, let's talk about the ReLU activation function. In 2D, ReLU kept only positive inputs, which was the first quadrant. In 3D, ReLU acts within the first octant, preserving only positive values and folding others into the axis plane.

After this, we apply another linear transformation back to 2D and add the bias. Notice how the data forms a unique shape or distribution, which is different from what we’d get with just linear transformations alone.

This demonstrates the critical role of the activation function in shaping the data and enhancing model performance.

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