.NN#4 — Neural Networks Decoded: Concepts Over Code

Author(s): RSD Studio.ai

Originally published on Towards AI.

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In our ongoing journey to decode the inner workings of neural networks, we’ve explored the fundamental building blocks — the perceptron, MLPs, and we’ve seen how these models harness the power of activation functions to tackle non-linear problems. But even with cleverly designed architectures and activation functions, a neural network is initially like a ship without a compass, aimlessly drifting on the ocean of data. How do we guide these complex systems towards our desired destination: accurate and reliable predictions?

The answer lies in loss functions.

Loss functions are the “guiding stars” of neural network training, providing a mathematical measure of how well (or how poorly) our model is performing. They quantify the difference between the network’s predictions and the actual, desired outputs. Essentially, a loss function is how the algorithm is able to know if the learning process has gone the correct way. It’s how well the model was able to get the output data when compared to actual values which are known as ground truths.

By analyzing the loss, neural networks can “learn from their mistakes,” adjusting their internal parameters (weights and biases) to gradually improve their performance and… Read the full blog for free on Medium.

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Published via Towards AI