Building Neural Networks from Scratch - Lecture 31 - Dropout layers in neural networks - Full code

In this video, we look at Dropout and how deactivating neurons randomly can prevent overfitting and lead to better generalization. 0:00 What is dropout? 8:03 Dropout layer and binomial distribution 12:06 Dropout layers on testing data 17:24 Dropout layer backward pass 19:23 Python code walkthrough 31:08 Visual demo of training with dropout ================================================= ✉️ Join our FREE Newsletter: https://vizuara.ai/our-newsletter/ ================================================= Vizuara philosophy: As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there. Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch. No cost. No hidden charges. Pure old school teaching and learning. ================================================= 🌟 Meet Our Team: 🌟 🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper) 🔗 LinkedIn: https://www.linkedin.com/in/raj-abhijit-dandekar-67a33118a/ 🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/rajat-dandekar-901324b1/ 🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/sreedath-panat-8a03b69a/ 🎓 Sahil Pocker (Machine Learning Engineer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/sahil-p-a7a30a8b/ 🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23) 🔗 LinkedIn: https://www.linkedin.com/in/abhijeet-singh-9a1881192/ 🎓 Sourav Jana (Software Developer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/souravjana131/