Uploaded November 2022 | Updated September 2026, 6 hours ago
Overfitting is one of the main problems we face when building neural networks. Before jumping into trying out fixes for over or underfitting, it is important to understand what it means, why it happens and what problems it causes for our neural networks. In this video, we will see how to implement all the regularization techniques we learned about hands-on. This includes, L1/L2 regularization and how to set up its paramaters, Dropout regularization, Data Augmentation and Early Stoppingg.
Previous lesson: youtu.be/p5zLW8hT-pA
Next lesson: youtu.be/2f_45VzKEfE
π Here is a lesson notes booklet that summarizes everything you learn in this course in diagrams and visualizations. You can get it here π misraturp.gumroad.com/l/fdl
π©βπ» You can get access to all the code I develop in this course here: github.com/misraturp/Deep-learning-course-repo
βTo get the most out of the course, don't forget to answer the end of module questions:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Questions-35797462583d43a3adcea9478bfe035d
π You can find the answers here:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Answers-f90449595add492fb9dd8db3859662e3
RESOURCES:
πββοΈ Data Science Kick-starter mini-course: misraturp.gumroad.com/l/kick-starter
πΌ Pandas cheat sheet: misraturp.gumroad.com/l/pandascs
π Fundamentals of Deep Learning in 25 pages: misraturp.gumroad.com/l/fdl
π Website - misraturp.com
π₯ Twitter - twitter.com/misraturp
Overfitting is one of the main problems we face when building neural networks. Before jumping into trying out fixes for over or underfitting, it is important to understand what it means, why it happens and what problems it causes for our neural networks. In this video, we will see how to implement all the regularization techniques we learned about hands-on. This includes, L1/L2 regularization and how to set up its paramaters, Dropout regularization, Data Augmentation and Early Stoppingg.
Previous lesson: youtu.be/p5zLW8hT-pA
Next lesson: youtu.be/2f_45VzKEfE
π Here is a lesson notes booklet that summarizes everything you learn in this course in diagrams and visualizations. You can get it here π misraturp.gumroad.com/l/fdl
π©βπ» You can get access to all the code I develop in this course here: github.com/misraturp/Deep-learning-course-repo
βTo get the most out of the course, don't forget to answer the end of module questions:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Questions-35797462583d43a3adcea9478bfe035d
π You can find the answers here:
https://fishy-dessert-4fc.notion.site/Deep-Learning-101-Answers-f90449595add492fb9dd8db3859662e3
RESOURCES:
πββοΈ Data Science Kick-starter mini-course: misraturp.gumroad.com/l/kick-starter
πΌ Pandas cheat sheet: misraturp.gumroad.com/l/pandascs
π Fundamentals of Deep Learning in 25 pages: misraturp.gumroad.com/l/fdl
π Website - misraturp.com
π₯ Twitter - twitter.com/misraturp
