> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/opencampus-machine-learning-program/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-6-natural-language-processing-part-i.md). # Week 7 - Working with Images using TorchVision ### This week you will... * Get more hands-on experience with TorchVision, in particular on loading images from folders, building preprocessing pipelines, and applying data augmentations * Get familiar wit transfer learning, a powerful technique to include the knowledge of models that were trained on large datasets and benefit from the features these models already learned in your own problem scenario. ### Learning Resources {% file src="/files/yGgZPKGS61Ugxs8cSq5g" %} ### Additional Learning Resources * [Video on Data Augmentation](https://www.youtube.com/watch?v=JI8saFjK84o\&list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF\&index=21) (10 min) * [Video on Transfer Learning](https://www.youtube.com/watch?v=FQM13HkEfBk\&list=PLkDaE6sCZn6Gl29AoE31iwdVwSG-KnDzF\&index=20) (9 min) ### Until next week you should... * watch the [videos of Module 4](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/533luly/introduction-to-efficient-data-pipelines) ("Efficient Training Pipelines") of course 2 ("PyTorch: Techniques and Ecosystem Tools")
* complete the exercises given in the assignment given [here](https://colab.research.google.com/github/opencampus-sh/course-material/blob/main/applied-machine-learning/week-06/Week6_Pneumonia_Assignment.ipynb).
* watch the videos "[Why human-level performance?](https://www.youtube.com/watch?v=J3HHOwcrkK8\&list=PLkDaE6sCZn6E7jZ9sN_xHwSHOdjUxUW_b\&index=8)", "[Avoidable bias](https://www.youtube.com/watch?v=CZf3oo0fuh0)", and "[Understanding human-level performance](https://www.youtube.com/watch?v=NUmbgp1h64E)" to help you evaluating and improving your model * consider a baseline model or a baseline comparison for your project task according to the instructions given [here](https://github.com/opencampus-sh/ml-project-template/tree/main/2_BaselineModel) * document the evaluation results of your baseline model and the used metric(s) in your project repository --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-6-natural-language-processing-part-i.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.