This document introduces Google Colaboratory (Colab), a cloud-based IDE that allows users to write and execute Python code in their browser. Some key points: - Colab allows coding in Python without needing to install anything locally by using GPU-enabled virtual machines in the cloud. - It is based on Jupyter notebooks and facilitates team collaboration by allowing sharing of notebooks. - Popular machine learning libraries are pre-installed, and files can be accessed from Google Drive for easy sharing of work. - The document demonstrates how to start a Colab notebook, access files from Drive, use Python libraries for tasks like classification, and shows examples classifying heart disease and images of dogs vs cats.

1. Introduction to Google
Colaboratory
Yomna Hassan
Misr International University

2. Why this session?
This is session is to introduce you to cloud based IDE’s such as
Colab and Jupyter. This is to help in facilitating your coding
experience while minimizing the amount of setup effort
needed to run standard AI and machine learning based
projects using Python.
We are focused on python as it is one of the main languages
focused on machine learning applications.

3. Survey - What’s your knowledge
level?
1- Python
2-Machine Learning
3- Google Colab

4. Difference between Jupyter and
Colab
Jupyter is the open source project on which Colab is based.
Colab allows you to use and share Jupyter notebooks with
others without having to download, install, or run anything.

5. Google Colab
In this session, we will focus on Google Colab. Colab has
already installed many of the popular libraries you may need
to run your code. In addition to this you can access and run
your code from anywhere with an internet connection.
Google Colab also facilitates teamwork/sharing between
multiple programmers.

6. Colab Pros and Cons
Pros:
1. Fast with no hassle GPU support
2. Can be accessed from anywhere
3. Leaves you free to work on your computer without using resources
4. File can be saved out as Jupiter file
5. Most popular libraries are pre-installed

7. Colab Pros and Cons
Cons:
1. Can be hard to upload local file
2. Files need to be downloaded which may take time if your internet
is slow

8. Today’s Program
1- How to start a Google Colab Notebook (file is saved automatically on
your google drive)
2- Running basic code with a “Local” file
3- Uploading files to Google Drive and connecting files to the code
4- Python Libraries
5- Simple Classifications Tasks :
- What is Classification
- CSV Data Classification
- Image Classification

9. New notebook
https://colab.research.google.com/
- Notebooks Location
- Playground mode
- External Files
- Github and Colab: ex: continualai
- Revisions, Show Diff
- Cells
- Sharing
- Comments

10. Colab Runtime
- What is runtime?
- GPU versus TPU
- Resources
Note:
Colab offers optional accelerated compute environments, including GPU and TPU. Executing code in a GPU or
TPU runtime does not automatically mean that the GPU or TPU is being utilized. To avoid hitting your GPU
usage limits, we recommend switching to a standard runtime if you are not utilizing the GPU. Choose
Runtime > Change Runtime Type and set Hardware Accelerator to None.
Colab is able to provide free resources in part by having dynamic usage limits that sometimes fluctuate,
and by not providing guaranteed or unlimited resources. This means that overall usage limits as well as
idle timeout periods, maximum VM lifetime, GPU types available, and other factors vary over time. Colab
does not publish these limits, in part because they can (and sometimes do) vary quickly.

11. Runtime Accelerators
● CPU: Central Processing Unit. Manage all the functions of a
computer.
● GPU: Graphical Processing Unit. Enhance the graphical
performance of the computer.
● TPU: Tensor Processing Unit. Custom build ASIC to accelerate
TensorFlow projects.

12. Python
Python is an interpreted high-level general-purpose
programming language.
Its language constructs as well as its object-oriented approach
aim to help programmers write clear, logical code for small
and large-scale projects.

13. Let’s start Coding!
- Basic line of code/ Printing (Input/Output consoles)
- Files Location
- Google Drive
- from google.colab import drive
drive.mount('/content/gdrive'
)
Try:
with open('/content/gdrive/My Drive/foo.txt'
, 'w') as f:
f.write('Hello Google Drive!'
)
!cat /content/gdrive/My Drive/foo.txt
Note: Upload Zipped folder and extract on drive

15. Classification

16. Type of Data
- Images/ Videos
- Tables / Structures

17. Datasets
UCI Machine Learning repository
Kaggle
Google Dataset Search
Visual Data
Tensor Flow Catalog
https://data.mendeley.com/

18. Python Libraries
Numpy : Matrices and numbers
PIL: Images
os: file systems
Scikit-learn: Machine learning
TensorFlow: machine learning

19. Terminologies
Data Frame
Algorithms:knn, kmeans , DT
Tuning
Features
Training
Testing
Accuracy
Epoch
Neural networks

20. KNN
The k-Nearest Neighbor classifier is by far the most simple machine learning and image
classification algorithm. It doesn’t actually “learn” anything. Instead, this algorithm
directly relies on the distance between feature vectors (which in our case, are the raw
RGB pixel intensities of the images).
k-NN algorithm classifies unknown data points by finding the most common class among
the k closest examples. Each data point in the k closest data points casts a vote, and the
category with the highest number of votes wins. Or, in plain English: “Tell me who your
neighbors are, and I’ll tell you who you are”

21. Choosing K Value - Bias-Variance
Tradoff
● The bias error is an error from erroneous assumptions in the learning
algorithm. High bias can cause an algorithm to miss the relevant
relations between features and target outputs (underfitting).
● The variance is an error from sensitivity to small fluctuations in the
training set. High variance may result from an algorithm modeling the
random noise in the training data (overfitting).

22. Choosing K Value

23. Writing Code
#Import Libraries
#Import dataset
#Shape dataset into dataframe /struct
#Design model
#Fit data model
#Compute accuracy
--------
Extra Steps: pre and post processing

24. Task 1 - Heart Disease Classification
Dataset: https://www.kaggle.com/ronitf/heart-disease-uci
Code:
https://colab.research.google.com/drive/19XE4497Gh-Xth
U9YE12ucuOPw3jywVtL

25. Exercise
Dataset:
https://www.kaggle.com/uciml/iris/tasks?taskId=1732
Code???

26. Text Versus Images

28. Task 2 - Image Classification
Dataset:
https://www.kaggle.com/c/dogs-vs-cats
Code:
https://colab.research.google.com/drive/1ZdRYZIMtMcBelTn4lCzx
aT9_ZD5Foe0s?usp=sharing

29. Calculate Accuracy

30. Downloading Data from Link Directly
on Colab
!wget -cq
https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flo
wer_data.zip
!unzip -qq flower_data.zip

31. Running Colab Code Offline
Colaboratory lets you connect to a local runtime using Jupyter. This allows you to
execute code on your local hardware and have access to your local file system.
Instructions for setting up Jupyter for the local runtime available here :
https://research.google.com/colaboratory/local-runtimes.html

32. Where to go from there?
Reading papers related to your topic to identify the most suitable tools/ methods to use
If you want to extend your knowledge about machine learning using python:
https://cognitiveclass.ai/courses/machine-learning-with-python
https://www.youtube.com/watch?v=ihK_YRMMHQM
https://www.analyticsvidhya.com/blog/2021/06/how-to-learn-mathematics-for-machin
e-learning-what-concepts-do-you-need-to-master-in-data-science/?fbclid=IwAR0gsww
28F2sppZ95LVAmQFEG2N0ohhsDMigE3g7HTiNG5ly_QYk30PMsQ4

33. Questions