> 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-3.md).

# Week 3 - Data Management and Neural Network Components

### This week you will...

* Set up a custom `Dataset` and `DataLoader` to load, transform, and batch data.
* Split the complete dataset into **Subsets for training, validation, and testing** and witness the impact of **Distribution Shift**.
* Explore why **Architectural Bias** in **Convolutional Neural Networks** acts as **Automated Feature Engineering** by learning **Convolution Filters** (or Kernels), allowing them to be more parameter-efficient compared to standard **Multi-Layer Perceptrons**.
* Understand what **Loss Surfaces** are and how **Gradient Descent** and batch size influence model optimization.

### Learning Resources

{% file src="/files/phpsMeOfe46Ypraf78DJ" %}

#### **Optional Resources**

* [MIT 6.S191: Introduction to Deep Learning](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI)\
  This is a full course and absolutely optional.\
  We recommed it as a resource **after completing DeepLearning.AI's** **PyTorch for Deep Learning**\
  (feel free to use it at your dicretion, if you want another perspective on e.g., CNNs.)
* [3Blue1Brown's Neural Network Playlist](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) (thanks for sharing!)<br>

#### **Additional Resources (going beyond this week's topics)**

* [Cross Validation](https://alan-turing-institute.github.io/Intro-to-transparent-ML-course/05-cross-val-bootstrap/cross-validation.html) (StatQuest Video, 6 min)\
  How to split the available data into subsets for training and testing and how **Cross Validation** is used to process different splits to evaluate your model's performance
* [Bias and Variance (Overfitting)](https://www.youtube.com/watch?v=EuBBz3bI-aA) (StatQuest, 7 min)\
  The video discusses the **Bias-Variance Tradeoff** and explains why we want to design and train models that are at the sweet spot between

  * **minimizing Bias (Underfitting)**\
    Error resulting from overly simple assumptions (the model misses the underlying pattern)
  * **minimizing Variance (Overfitting)**\
    Error resulting from the model being too sensitive to the training data.\
    High variance causes the model to "memorize" noise instead of learning the underlying distribution such that it fails to generalize on unseen data

  **Mini-Batch Gradient Descent** helps us navigate this tradeoff by introducing just enough noise to prevent the high variance (overfitting) typically seen in **Full-Batch Gradient Descent**

### Until next week you should...

* Complete [module 3](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/jcc479/introduction-to-nlp-with-pytorch) of the second course "PyTorch: Techniques and Ecosystem Tools"
* Set up a repository for your project following the instructions given [here](https://opencampus.gitbook.io/opencampus-machine-learning-program/projects/requirements)
* Conduct a literature review according to the instructions given [here](https://github.com/opencampus-sh/ml-project-template/blob/main/0_LiteratureReview/INSTRUCTIONS.md)
* Document your findings on the literature review according to the instructions of above
* Complete the assignment in the following notebook:
  * [Assignment Notebook](https://colab.research.google.com/github/opencampus-sh/course-material/blob/main/applied-machine-learning/week-03/Week3_AG_News_Assignment.ipynb)
  * You can find the helper functions required to run the notebook [here](https://github.com/opencampus-sh/course-material/blob/main/applied-machine-learning/week-03/homework_helper_utils.py). You can either load it the file into Colab or simply copy the code into a cell.


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