video1.0<iframe src="https://www.loom.com/embed/7474d8c38f9c4c5bb09225761aef0c0d" frameborder="0" width="1680" height="1260" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12601680Loomhttps://www.loom.com12601680https://cdn.loom.com/sessions/thumbnails/7474d8c38f9c4c5bb09225761aef0c0d-ee949423959aa363.gif358.639In this video, I discuss the fundamental concepts of probability as they relate to deep learning, covering topics such as sample spaces, conditional probability, and Bayes' theorem. I also explain the significance of random variables, expected value, variance, and key distributions like Bernoulli and Gaussian. Additionally, I delve into information theory, maximum likelihood estimation, and the role of sampling methods in deep learning. I encourage you to implement Softmax, CrossEntropyLoss, and DropoutLayer from scratch as practical exercises to reinforce your understanding. Thank you for your attention!