Batch Normalization in Deep Learning | Batch Learning in Keras

This video explores how Batch Normalization transforms the internal workings of neural networks by normalizing inputs within each mini-batch. By maintaining stable activations throughout the training process, Batch Normalization improves convergence speed and aids in tackling the vanishing/exploding gradient problem. Digital Notes for Deep Learning: https://shorturl.at/NGtXg Code - https://colab.research.google.com/drive/1473vOd0lCPbRW-co_Rm-_TBXgeajkJZ_?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! ⌚Time Stamps⌚ 00:00 - Intro 00:40 - What is Batch Normalization 03:11 - Why use batch Normalization 06:58 - Internal Co-Variate Shift 14:40 - Batch Normalization - The How? 31:41 - Batch Normalization During Test 35:32 - The Advantages 39:47 - Keras Implementation 43:24 - Outro #DeepLearning #BatchNormalization