PyTorch Tutorial 06 - Training Pipeline: Model, Loss, and Optimizer
New Tutorial series about Deep Learning with PyTorch!
⭐ Check out Tabnine, the FREE AI-powered code completion tool I use to help me code faster: https://www.tabnine.com/?utm_source=youtube.com&utm_campaign=PythonEngineer *
In this part we improve the code from the last part and will learn how a complete training pipeline is implemented in PyTorch. We replace the manually computed loss and weight updates with a loss and an optimizer from the PyTorch framework, which can do the optimization for us. We will then see how a PyTorch model is implemented and used for the forward pass.
- Training Pipeline in PyTorch
- Model Design
- Loss and Optimizer
- Automatic Training steps with forward pass, backward pass, and weight updates
Part 06: Training Pipeline: Model, Loss, and Optimizer
📚 Get my FREE NumPy Handbook:
https://www.python-engineer.com/numpybook
📓 Notebooks available on Patreon:
https://www.patreon.com/patrickloeber
⭐ Join Our Discord : https://discord.gg/FHMg9tKFSN
If you enjoyed this video, please subscribe to the channel!
Official website:
https://pytorch.org/
Part 01:
https://youtu.be/EMXfZB8FVUA
Linear Regression from scratch:
https://youtu.be/4swNt7PiamQ
Code for this tutorial series:
https://github.com/python-engineer/pytorchTutorial
You can find me here:
Website: https://www.python-engineer.com
Twitter: https://twitter.com/python_engineer
GitHub: https://github.com/python-engineer
#Python #DeepLearning #Pytorch
----------------------------------------------------------------------------------------------------------
* This is a sponsored 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! 🙏
Видео PyTorch Tutorial 06 - Training Pipeline: Model, Loss, and Optimizer канала Python Engineer
⭐ Check out Tabnine, the FREE AI-powered code completion tool I use to help me code faster: https://www.tabnine.com/?utm_source=youtube.com&utm_campaign=PythonEngineer *
In this part we improve the code from the last part and will learn how a complete training pipeline is implemented in PyTorch. We replace the manually computed loss and weight updates with a loss and an optimizer from the PyTorch framework, which can do the optimization for us. We will then see how a PyTorch model is implemented and used for the forward pass.
- Training Pipeline in PyTorch
- Model Design
- Loss and Optimizer
- Automatic Training steps with forward pass, backward pass, and weight updates
Part 06: Training Pipeline: Model, Loss, and Optimizer
📚 Get my FREE NumPy Handbook:
https://www.python-engineer.com/numpybook
📓 Notebooks available on Patreon:
https://www.patreon.com/patrickloeber
⭐ Join Our Discord : https://discord.gg/FHMg9tKFSN
If you enjoyed this video, please subscribe to the channel!
Official website:
https://pytorch.org/
Part 01:
https://youtu.be/EMXfZB8FVUA
Linear Regression from scratch:
https://youtu.be/4swNt7PiamQ
Code for this tutorial series:
https://github.com/python-engineer/pytorchTutorial
You can find me here:
Website: https://www.python-engineer.com
Twitter: https://twitter.com/python_engineer
GitHub: https://github.com/python-engineer
#Python #DeepLearning #Pytorch
----------------------------------------------------------------------------------------------------------
* This is a sponsored 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! 🙏
Видео PyTorch Tutorial 06 - Training Pipeline: Model, Loss, and Optimizer канала Python Engineer
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