How does an AI learn? Training Neural Networks with Backpropagation

This article was co-written by Oanottage.

Drawing by MalachkovaK

In this series of articles, we’re going to explore Neural Networks from the ground up. No fancy jargon, just a down-to-earth exploration of what they are, how they work, and where they come from. Together, we’ll unravel the mysteries, demystify the complexities, and gain a solid grasp of this cutting-edge field. In my former article, I gave an easy-to-understand explanation of feed-forward Neural Networks.

In my former article, I explained:

• What Feedforward Neural Networks are
• What the iconic MNIST Dataset is
• How to build an FFNN to process the MNIST Dataset

This article can be found here:

Understanding Feed Forward Neural Networks with MNIST Dataset

The concept of a NN is nice and fun, but it is only useful when it gives valuable outputs. This is where the training process becomes essential, enabling neural networks to learn from data, make predictions, and perform complex tasks.