new colab workflow

Author: kevinzakka

assignments/2020/assignment2.md

@@ -36,7 +36,7 @@ In this assignment you will practice writing backpropagation code, and training
 - Implement various **update rules** used to optimize Neural Networks.
 - Implement **Batch Normalization** and **Layer Normalization** for training deep networks.
 - Implement **Dropout** to regularize networks.
-- Understand the architecture of **Convolutional Neural Networks** and get practice with training these models on data.
+- Understand the architecture of **Convolutional Neural Networks** and get practice with training them.
 - Gain experience with a major deep learning framework, such as **TensorFlow** or **PyTorch**.
 
 ### Setup
@@ -51,9 +51,9 @@ You can work on the assignment in one of two ways: **remotely** on Google Colabo
 
 If you choose to work with Google Colab, please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory).
 
-**Note 1**. Please make sure that you work on the Colab notebooks in the order of the questions (see below). The reason is that the code cells that get executed *at the end* of the notebooks save the modified files back to your drive and some notebooks may require code from previous notebook.
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/IZUz4pRYlus" frameborder="0" allowfullscreen></iframe>
 
-**Note 2**. Related to above, ensure you are periodically saving your notebook (`File -> Save`), and any edited `.py` files relevant to that notebook (i.e. **by executing the last code cell**) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+**Note**. Ensure you are periodically saving your notebook (`File -> Save`) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
 
 Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
 

assignments/2020/assignment2_colab.zip

@@ -26,11 +26,11 @@ like GPUs (K80, P100) and TPUs which will be particularly useful for assignments
 3. At the top, select `Connect more apps` which should bring up a `GSuite Marketplace` window.
 4. Search for **Colab** then click `Add`.
 
-**Workflow**. Every assignment provides you with a download link to a zip file containing Colab notebooks and Python starter code. You can upload the folder to Drive, open the notebooks in Colab and work on them, then save your progress back to Drive. We encourage you to watch the tutorial video below which covers the recommended workflow for assignment 1.
+**Workflow**. Every assignment provides you with a download link to a zip file containing Colab notebooks and Python starter code. You can upload the folder to Drive, open the notebooks in Colab and work on them, then save your progress back to Drive. We encourage you to watch the tutorial video below which covers the recommended workflow using assignment 1 as an example.
 
-<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/qvwYtun1uhQ" frameborder="0" allowfullscreen></iframe>
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/IZUz4pRYlus" frameborder="0" allowfullscreen></iframe>
 
-**Best Practices**. There are a few things you should be aware of when working with Colab. The first thing to note is that resource limits aren't guaranteed (this is the price for being free). If you are idle for a certain amount of time or your total connection time exceeds the maximum allowed time (~12 hours), the Colab VM will disconnect. This means any unsaved progress will be lost. <font color="red"><strong>Thus, get into the habit of frequently saving your code whilst working on assignments.</strong></font> To read more about resource limitations in Colab, read their FAQ [here](https://research.google.com/colaboratory/faq.html).
+**Best Practices**. There are a few things you should be aware of when working with Colab. The first thing to note is that resources aren't guaranteed (this is the price for being free). If you are idle for a certain amount of time or your total connection time exceeds the maximum allowed time (~12 hours), the Colab VM will disconnect. This means any unsaved progress will be lost. <font color="red"><strong>Thus, get into the habit of frequently saving your code whilst working on assignments.</strong></font> To read more about resource limitations in Colab, read their FAQ [here](https://research.google.com/colaboratory/faq.html).
 
 **Using a GPU**. Using a GPU is as simple as switching the runtime in Colab. Specifically, click `Runtime -> Change runtime type -> Hardware Accelerator -> GPU` and your Colab instance will automatically be backed by GPU compute.