Curated Notebooks

This notebook contains an example of using the Gemini API to analyze a a product sketch (in this case, a drawing of a Jet Backpack), create a marketing campaign for it, and output taglines in JSON format.

This notebook provides an example of how to prompt Gemini 1.5 Pro using an audio file. In this case, you'll use a sound recording of President John F. Kennedy’s 1961 State of the Union address.

System instructions allow you to steer the behavior of the model. By setting the system instruction, you are giving the model additional context to understand the task, provide more customized responses, and adhere to guidelines over the user interaction.

Using function calling allows you to control how the Gemini API acts when tools have been specified.

This notebook provides a quick example of how to prompt Gemini 1.5 Pro using a text file. In this case, you'll use a 400 page transcript from Apollo 11.

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 this self-paced lab from Google Cloud to inspect rich documents with Gemini.

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.

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.

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.

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.

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).

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.

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

cuDF is a Python GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API in the style of pandas.

This notebook introduces how to work with timestamps, time intervals, periods, time deltas, and durations.

Getting started with data analysis on colab using python

Programmatic Google Colab Notebook Series (2018-2023)

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.