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TABLE OF CONTENTS

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Getting started more_vert

Data science more_vert

Machine learning more_vert

More resources more_vert
Featured examples more_vert
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WELCOME TO COLAB!


(NEW) TRY THE GEMINI API

 * Generate a Gemini API key
 * Talk to Gemini with the Speech-to-Text API
 * Gemini API: Quickstart with Python
 * Gemini API code sample
 * Compare Gemini with ChatGPT
 * More notebooks

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If you're already familiar with Colab, check out this video to learn about
interactive tables, the executed code history view and the command palette.

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WHAT IS COLAB?

Colab, or ‘Colaboratory’, allows you to write and execute Python in your
browser, with

 * Zero configuration required
 * Access to GPUs free of charge
 * Easy sharing

Whether you're a student, a data scientist or an AI researcher, Colab can make
your work easier. Watch Introduction to Colab to find out more, or just get
started below!

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GETTING STARTED

The document that you are reading is not a static web page, but an interactive
environment called a Colab notebook that lets you write and execute code.

For example, here is a code cell with a short Python script that computes a
value, stores it in a variable and prints the result:

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seconds_in_a_day = 24 * 60 * 60
seconds_in_a_day




86400



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To execute the code in the above cell, select it with a click and then either
press the play button to the left of the code, or use the keyboard shortcut
'Command/Ctrl+Enter'. To edit the code, just click the cell and start editing.

Variables that you define in one cell can later be used in other cells:

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seconds_in_a_week = 7 * seconds_in_a_day
seconds_in_a_week




604800



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Colab notebooks allow you to combine executable code and rich text in a single
document, along with images, HTML, LaTeX and more. When you create your own
Colab notebooks, they are stored in your Google Drive account. You can easily
share your Colab notebooks with co-workers or friends, allowing them to comment
on your notebooks or even edit them. To find out more, see Overview of Colab. To
create a new Colab notebook you can use the File menu above, or use the
following link: Create a new Colab notebook.

Colab notebooks are Jupyter notebooks that are hosted by Colab. To find out more
about the Jupyter project, see jupyter.org.

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DATA SCIENCE

With Colab you can harness the full power of popular Python libraries to analyse
and visualise data. The code cell below uses numpy to generate some random data,
and uses matplotlib to visualise it. To edit the code, just click the cell and
start editing.

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import numpy as np
import IPython.display as display
from matplotlib import pyplot as plt
import io
import base64

ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]

fig = plt.figure(figsize=(4, 3), facecolor='w')
plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)
plt.title("Sample Visualization", fontsize=10)

data = io.BytesIO()
plt.savefig(data)
image = F"data:image/png;base64,{base64.b64encode(data.getvalue()).decode()}"
alt = "Sample Visualization"
display.display(display.Markdown(F"""![{alt}]({image})"""))
plt.close(fig)





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You can import your own data into Colab notebooks from your Google Drive
account, including from spreadsheets, as well as from GitHub and many other
sources. To find out more about importing data, and how Colab can be used for
data science, see the links below under Working with data.

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MACHINE LEARNING

With Colab you can import an image dataset, train an image classifier on it, and
evaluate the model, all in just a few lines of code. Colab notebooks execute
code on Google's cloud servers, meaning you can leverage the power of Google
hardware, including GPUs and TPUs, regardless of the power of your machine. All
you need is a browser.

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Colab is used extensively in the machine learning community with applications
including:

 * Getting started with TensorFlow
 * Developing and training neural networks
 * Experimenting with TPUs
 * Disseminating AI research
 * Creating tutorials

To see sample Colab notebooks that demonstrate machine learning applications,
see the machine learning examples below.

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MORE RESOURCES


WORKING WITH NOTEBOOKS IN COLAB

 * Overview of Colaboratory
 * Guide to markdown
 * Importing libraries and installing dependencies
 * Saving and loading notebooks in GitHub
 * Interactive forms
 * Interactive widgets


WORKING WITH DATA

 * Loading data: Drive, Sheets and Google Cloud Storage
 * Charts: visualising data
 * Getting started with BigQuery


MACHINE LEARNING CRASH COURSE

These are a few of the notebooks from Google's online machine learning course.
See the full course website for more.

 * Intro to Pandas DataFrame
 * Linear regression with tf.keras using synthetic data


USING ACCELERATED HARDWARE

 * TensorFlow with GPUs
 * TensorFlow with TPUs

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FEATURED EXAMPLES

 * NeMo voice swap: Use Nvidia NeMo conversational AI toolkit to swap a voice in
   an audio fragment with a computer-generated one.

 * Retraining an Image Classifier: Build a Keras model on top of a pre-trained
   image classifier to distinguish flowers.

 * Text Classification: Classify IMDB film reviews as either positive or
   negative.

 * Style Transfer: Use deep learning to transfer style between images.

 * Multilingual Universal Sentence Encoder Q&A: Use a machine-learning model to
   answer questions from the SQuAD dataset.

 * Video Interpolation: Predict what happened in a video between the first and
   the last frame.

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