Notebooks – colab.google

Gemini API

Talk to Gemini with the Vertex Speech-to-Text API

Having a spoken conversation with Gemini, Google's latest and most advanced model, is simple in a Colab notebook with the Vertex Speech-to-Text API.

Compare Gemini and ChatGPT responses

Use Google's latest model release, Gemini, to teach you what you want to know and compare those with ChatGPT's responses. The models are specifically prompted not to generate extra text to make it easier to compare any differences.

Use Gemini to prompt Stable Diffusion

Finding the right words to prompt an image generator can be a chore. Use Google's latest model release, Gemini, to prompt Stable Diffusion to produce amazing generated imagery.

AI & Machine Learning

Music Transcription with Transformers

Interactive demo of a few music transcription models created by Google's Magenta team. You can upload audio and have one of our models automatically transcribe it.

Generating Music with Transformers

This Colab notebook lets you play with pretrained Transformer models for piano music generation, based on the Music Transformer model introduced by Huang et al. in 2018.

Text Classification with Movie Reviews

This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem.

Multilingual Universal Sentence Encoder Q&A Retrieval

Demo for using Universal Encoder Multilingual Q&A model for question-answer retrieval of text, illustrating the use of question_encoder and response_encoder of the model.

Create and Train a Custom RL Agent

This colab demonstrates how to create a variant of a provided agent (Example 1) and how to create a new agent from scratch (Example 2).

Visualize RL Agent Training on TensorBoard

This colab allows you to easily view the trained baselines with Tensorboard (even if you don't have Tensorboard on your local machine!). Simply specify the game you would like to visualize and then run the cells in order.

Hyperparameter Tuning with Tensorboard

The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.

Data & Analytics

RAPIDS cuDF for accelerated data science on Google Colab

Use RAPIDS cuDF and GPUs to turbocharge your data analysis work.

Exploratory Data Analysis Intro

Getting started with data analysis on colab using python

Advanced Business Analytics and Mathematics

Programmatic Google Colab Notebook Series (2018-2023)

Twitter Pulse Checker

This is a quick and dirty way to get a sense of what's trending on Twitter related to a particular Topic. For my use case, I am focusing on the city of Seattle but you can easily apply this to any topic.

Cloud Computing

Colab + BigQuery — Perfect Together

The goal of this Colab notebook is to highlight some benefits of using Google BigQuery and Colab together to perform some common data science tasks.

Online prediction with BigQuery ML

In this tutorial, you learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.

Serving PyTorch image models with prebuilt containers on Vertex AI

In this tutorial, you learn how to package and deploy a PyTorch image classification model using a prebuilt Vertex AI container with TorchServe for serving online and batch predictions.

AutoML training tabular binary classification model for batch explanation

In this tutorial, you learn to use AutoML to create a tabular binary classification model from a Python script, and then learn to use Vertex AI Batch Prediction to make predictions with explanations.

Data Visualization

Explore Patent Database with ML

Patent landscaping is an analytical approach commonly used by corporations, patent offices, and academics to better understand the potential technical coverage of a large number of patents where manual review (i.e., actually reading the patents) is not feasible due to time or cost constraints.

mediapy

Read, write, and show images and videos in a Colab notebook

Visualize Chemical Structures in a Notebook

Molecules can be represented as strings with SMILES. Simplified molecular-input line-entry system (SMILES) is a string based representation of a molecule.

Exploratory Data Analysis with Python

Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics and, usually, plotting them visually.

Education

Colab Primer

Quick primer on Colab and Jupyter notebooks

Intro Python Tutorial

Stanford CS231n Python Tutorial With Google Colab

Advanced Python Tutorial

In this tutorial, we will be exploring some advanced Python concepts and techniques using Google Colab.

Fun

Fast Style Transfer for Arbitrary Styles

Based on the model code in magenta and the publication: Exploring the structure of a real-time, arbitrary neural artistic stylization network.

Brax - Physics Environments for Simulations

Brax simulates physical systems made up of rigid bodies, joints, and actutators.

Predict Shakespeare with Keras+CloudTPU

This example uses tf.keras to build a language model and train it on a Cloud TPU. This language model predicts the next character of text given the text so far. The trained model can generate new snippets of text that read in a similar style to the text training data.

Science

AlphaFold

This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of AlphaFold v2.3.2.

AlphaTensor

This Colab shows how to load the provided .npz file with rank- 49 factorizations of 𝓣4 in standard arithmetic, and how to compute the invariants ℛ and 𝒦 in order to demonstrate that these factorizations are mutually nonequivalent.

Google Earth API

This notebook demonstrates how to setup the Earth Engine Python API in Colab and provides several examples of how to print and visualize Earth Engine processed data.

Molecular Dynamics Simulations

Notebook for running Molecular Dynamics (MD) simulations using OpenMM engine and AMBER force field for PROTEIN systems. This notebook is a supplementary material of the paper "Making it rain: Cloud-based molecular simulations for everyone" (link here) and we encourage you to read it before using this pipeline.

Curated Notebooks