PyTorch Tutorials - Complete Beginner Course - PyTorch LR Scheduler - Adjust The Learning Rate For Better Results

In this PyTorch Tutorial we learn how to use a Learning Rate (LR) Scheduler to adjust the LR during training. Models often benefit from this technique once learning stagnates, and you get better results. We will go over the different methods we can use and I'll show some code examples that apply the scheduler. ~~~~~~~~~~~~~~ GREAT PLUGINS FOR YOUR CODE EDITOR ~~~~~~~~~~~~~~ ✅ Write cleaner code with Sourcery: https://sourcery.ai/?utm_source=youtube&utm_campaign=pythonengineer * Documentation: https://pytorch.org/docs/stable/optim.html - torch.optim.lr_scheduler provides several methods to adjust the learning rate based on the number of epochs. - torch.optim.lr_scheduler.ReduceLROnPlateau allows dynamic learning rate reducing based on some validation measurements. Get my Free NumPy Handbook: https://www.python-engineer.com/numpybook 🦾 Join Our Discord : https://discord.gg/FHMg9tKFSN 📓 ML Notebooks available on Patreon: https://www.patreon.com/patrickloeber If you enjoyed this video, please subscribe to the channel: ▶️ : https://www.youtube.com/channel/UCbXgNpp0jedKWcQiULLbDTA?sub_confirmation=1 ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: https://www.python-engineer.com 🐦 Twitter - https://twitter.com/patloeber 📸 Instagram - https://www.instagram.com/patloeber 🦾 Discord: https://discord.gg/FHMg9tKFSN 💻 GitHub: https://github.com/patrickloeber ~~~~~~~~~~~~~~ SUPPORT ME ~~~~~~~~~~~~~~ 🅿 Patreon - https://www.patreon.com/patrickloeber Music: https://www.bensound.com/ #Python ---------------------------------------------------------------------------------------------------------- * This is a sponsored or an affiliate link. By clicking on it you will not have any additional costs, instead you will support me and my project. Thank you so much for the support! 🙏