100 Days of Deep Learning - Dropout Layer in Deep Learning | Dropouts in ANN | End to End Deep Learning

Dropout is an approach to regularization in neural networks which helps reduce interdependent learning amongst the neurons. Dropout is used as a regularization technique in order to reduce overfitting. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes Dropouts original paper - https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ✨ Hashtags✨ #DeepLearningExplained #DropoutLayers #NeuralNetworks101 ⌚Time Stamps⌚ 00:00 - Intro 01:25 - Problem of Overfitting 03:52 - Solutions for Overfitting 06:08 - Dropouts 13:14 - Why it works? 18:50 - Random Forest Analogy 23:45 - How prediction works? 27:10 - Outro