100 Days of Deep Learning - Regularization in Deep Learning | L2 Regularization in ANN | L1 Regularization | Weight Decay in ANN

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52 hours of video
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keras deep learning machine learning artificial neural network neural network neural net sequential model relu educational data augmentation dropout regularization explained

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Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes Code - https://colab.research.google.com/drive/1PObj5KrXLDDmHjoJ1x0bVmxAFbif5s7q?usp=sharing ============================ 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 #RegularizationTechniques #NeuralNetworksTips ⌚Time Stamps⌚ 00:00 - Intro 00:40 - Improving NN Performance 01:53 - Overfitting 04:52 - Why Neural networks overfit? 09:08 - Ways to solve overfitting 12:10 - Regularization 19:40 - Intuition behind Regularization 26:38 - Code Demo 35:35 - Outro