video1.0<iframe src="https://www.loom.com/embed/4809c8958f48431cba8e05280c926765" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/4809c8958f48431cba8e05280c926765-4a028b5fcc0a74fc.gif285.087In this video, I explain the probability theory that underlies neural networks, emphasizing that they are essentially probability machines estimating conditional probabilities. I discuss how the softmax function converts logits into a valid probability distribution, ensuring that all outputs sum to 1. We also explore concepts like cross-entropy loss, Bayes' theorem, and the importance of minimizing uncertainty through entropy. Additionally, I touch on key distributions used in classification tasks and the role of batch normalization in stabilizing training. I encourage you to reflect on these concepts as we continue to deepen our understanding of neural networks.