{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/4809c8958f48431cba8e05280c926765\" frameborder=\"0\" width=\"1680\" height=\"1260\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1260,"width":1680,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1260,"thumbnail_width":1680,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/4809c8958f48431cba8e05280c926765-4a028b5fcc0a74fc.gif","duration":285.087,"title":"6-Understanding Probability Theory in Neural Networks 🤖","description":"In 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."}