> 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-1.md).

# Week 1 - General Introduction

### This week you will...

* get a basic introduction to neural nets in order to get a first intuition in the underlying mechanisms
* get a first idea about possible projects you might want to work on throughout the course

### Learning Resources

{% file src="/files/K2jsWkbFuySVVEC17LWP" %}

{% file src="/files/GsGU1allu5QT1CaZnrdL" %}

* Video [Neural Networks Explained](https://www.youtube.com/watch?v=GvQwE2OhL8I) (12 minutes)
* [Introductory course on Python](https://www.kaggle.com/learn/python) from Kaggle
* [Tutorial on Colab](https://medium.com/towards-artificial-intelligence/google-colab-101-tutorial-with-python-tips-tricks-and-faq-7689bd4d24b4) on Medium

### Until next week you should...

* enroll for the course "[PyTorch for Deep Learning Professional Certificate](https://www.deeplearning.ai/courses/pytorch-for-deep-learning-professional-certificate/)" at DeepLearnig.AI using the free plan
* complete [Module 1](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/k227hi9c/conversation-between-laurence-moroney-and-andrew-ng) and [Module 2](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/0kkxsl/decoding-a-secret-message) of the first course "PyTorch: Fundamentals"
* think about project ideas to present in class next week (post your ideas in the chat during the week)
* work your way through the the following notebook and complete the assignments:
  * [Assignment Notebook](https://colab.research.google.com/github/opencampus-sh/course-material/blob/main/applied-machine-learning/week-01/Week1_Notebook1_Ice_Cream_Sales.ipynb)


---

# 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-1.md?ask=<question>&goal=<endgoal>
```

`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.