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|[00 - PyTorch Fundamentals](https://www.learnpytorch.io/00_pytorch_fundamentals/)| Many fundamental PyTorch operations used for deep learning and neural networks. |[Go to exercises & extra-curriculum](https://www.learnpytorch.io/00_pytorch_fundamentals/#exercises)|[Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/00_pytorch_and_deep_learning_fundamentals.pdf)|
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|[01 - PyTorch Workflow](https://www.learnpytorch.io/01_pytorch_workflow/)| Provides an outline for approaching deep learning problems and building neural networks with PyTorch. |[Go to exercises & extra-curriculum](https://www.learnpytorch.io/01_pytorch_workflow/#exercises)|[Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/01_pytorch_workflow.pdf)|
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|[02 - PyTorch Neural Network Classification](https://www.learnpytorch.io/02_pytorch_classification/)| Uses the PyTorch workflow from 01 to go through a neural network classification problem. |[Go to exercises & extra-curriculum](https://www.learnpytorch.io/02_pytorch_classification/#exercises)|[Go to slides](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/slides/02_pytorch_classification.pdf)|
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|[03 - PyTorch Computer Vision](https://www.learnpytorch.io/03_pytorch_computer_vision/)| Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. | Go to exercises & extra-curriculum | Go to slides |
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|Coming soon: 04 - PyTorch Custom Datasets | How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | Go to exercises & extra-curriculum | Go to slides |
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|[03 - PyTorch Computer Vision](https://www.learnpytorch.io/03_pytorch_computer_vision/)| Let's see how PyTorch can be used for computer vision problems using the same workflow from 01 & 02. |[Go to exercises & extra-curriculum](https://www.learnpytorch.io/03_pytorch_computer_vision/#exercises)| Go to slides |
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|[04 - PyTorch Custom Datasets](https://www.learnpytorch.io/04_pytorch_custom_datasets/)| How do you load a custom dataset into PyTorch? Also we'll be laying the foundations in this notebook for our modular code (covered in 05). | Go to exercises & extra-curriculum | Go to slides |
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| Coming soon: 05 - Going modular | PyTorch is designed to be modular, let's turn what we've created into a series of Python scripts (this is how you'll often find PyTorch code in the wild). | Go to exercises & extra-curriculum | Go to slides |
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| Coming soon: 06 - PyTorch Transfer Learning | Let's take a well performing pre-trained model and adjust it to one of our own problems. | Go to exercises & extra-curriculum | Go to slides |
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| Coming soon: 07 - Milestone Project 1: PyTorch Experiment Tracking | We've built a bunch of models... wouldn't it be good to track how they're all going? | Go to exercises & extra-curriculum | Go to slides |
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| Coming soon: 08 - Milestone Project 2: PyTorch Paper Replicating | PyTorch is the most popular deep learning framework for machine learning research, let's see why by replicating a machine learning paper. | Go to exercises & extra-curriculum | Go to slides |
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| Coming soon: 09 - Milestone Project 3: Model deployment | So you've built a working PyTorch model... how do you get it in the hands of others? Hint: deploy it to the internet. | Go to exercises & extra-curriculum | Go to slides |
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### Old outline version (will update this if necessary)
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6. PyTorch transfer learning - Let's improve upon the models we've built ourselves using transfer learning.
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7. PyTorch experiment tracking - We've built a bunch of models... wouldn't it be good to track how they're all going?
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8. PyTorch paper replicating - Let's see why PyTorch is the most popular deep learning framework for machine learning research by replicating a machine learning research paper with it.
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9. PyTorch model deployment - How do you get your PyTorch models in the hands of others?
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Each notebook will teach a maximum of 3 big ideas.
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## Status
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***Working on:** exercises and solutions for notebook 03, 04
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***Working on:** exercises and solutions for notebook 04
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***Total video count:** 90
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***Next:**annotate notebooks 03, 04, 05 with text then make slides and images
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***Next:**finish exericses/solutions for 04, then record videos for 03, 04
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