Batch Normalization in Deep Learning | Batch Learning in Keras

Bystaff



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.

Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes

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

source

Similar Posts