Using Google NotebookLM for Data Science: A Complete Guide

As the world of data science continually evolves, the tools and technologies used by professionals in the field also advance. Google's NotebookLM offers a unique and powerful way to understand your data and information. This blog post delves into what NotebookLM is, how it works, and the many possibilities it opens up for data science researchers.

Google's new experimental product, NotebookLM, is based on the latest advances in large language models. It is similar to other Large Language Model (LLM)-powered applications, such as ChatPDF, ChatGPT, and Poe, which allow users to upload data files and generate questions. These applications offer the same features and capabilities.

So why is it special?

NotebookLM is a specialized application that allows you to upload up to 10 documents. You can easily upload your sources, which can include Google Docs, PDF files from your computer, or any text content that is less than 50,000 words.

NotebookLM addresses the limitations of using ChatGPT and Poe. It allows you to upload more than three documents and understand large documents in seconds.

Using NotebookLM is simple. You can upload Google Docs, PDF files from your computer, or any text content in seconds. Once your sources are uploaded, NotebookLM becomes your go-to tool for creative brainstorming and consultation.

First, we will go to the website “notebooklm.google.com” and create a Project.

I have downloaded PDF files of popular research articles on reinforcement learning:

1. Continuous control with deep reinforcement learning
2. Play Atari with deep reinforcement learning
3. Deep reinforcement learning with double Q-learning

Then we will load these PDF files into our project one by one.

After uploading the files, we select those to use as context.

Summary

We will select the research paper “Continuous Control with Deep Reinforcement Learning” and ask NotebookLM to summarize it for us.

Question: “Could you summarize the research work for me? Try using bullet points.”

It only took a few seconds to get a response. More questions were also offered.

Terminology extraction

We will ask you to create a list of key terms used in the article.

Message: “Create the list of key terms used in this document.”

Not only did it provide us with key terms, but it also indicated their location within the document.

Analysis of reinforcement learning

Now we will use the three articles to understand the research trend.

Message: “Discuss the three research articles and provide an analysis of the current state of research on reinforcement learning.”

It worked very well.

Creative assistance

Now we'll use it and ask the ai ​​to help us decide the title of a senior project that will secure us a job as a machine learning engineer.

Message: “Using three articles, generate a new research title that will help me get a job as a research reinforcement engineer.”

It's good. But not great.

Quotes

Ask any question about your sources and NotebookLM will respond with answers, complete with citations from those documents.

Document guide

When you upload a new source, NotebookLM creates a “source guide” that summarizes the document and suggests key topics and questions.

Take notes

Each notebook contains a notes section, where you can jot down ideas or information discovered by NotebookLM.

• Device Compatibility: NotebookLM is currently best enjoyed on a desktop computer.
• Access restrictions: Initially available only in the US and for personal Google accounts.
• Content Limitations: Each notebook can contain ten sources and one note, and each source is limited to 50,000 words.

• Collaborative features: Notebooks can be shared with colleagues or classmates, offering viewer or editor access.
• Interacting with multiple sources: Users can switch between interacting with a single source or all sources in a Notebook.

NotebookLM is in its initial testing phase and is currently free. Access is slowly opening up to small groups of people, with a registration option available for those interested in joining the waiting list.

While NotebookLM presents interesting opportunities, it is essential to consider what content to upload. Avoid documents that contain personal or sensitive information. Also, please note that it is an experimental project and is currently limited to those in the Early Access Program.

Google's NotebookLM is a significant advancement in the way data scientists and professionals decipher complex information. Since most of our information is in PDF files and stored on computers, NotebookLM allows you to understand your legal contract by simply adding all the files and asking essential questions. Although NotebookLM lacks some features and precision compared to ChatGPT, it has great potential to become an essential tool in your workspace as it continues to evolve.

Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.