Google Colaboratory, which is commonly referred to as “Colab,” is a cloud-based Jupyter notebook environment that runs in your web browser. It enables anyone with internet access to experiment with machine learning and coding for artificial intelligence. Colab allows you to write and execute Python code, collaborate simultaneously with other team members, and combine your work into a single notebook that includes rich text, charts, images, HTML, and LaTeX.
Artificial Intelligence and Machine Learning: A Quick Primer
Artificial intelligence (AI) is hardware that imitates the capabilities of the human brain. Machine learning (ML), on the other hand, is a subset of AI that uses algorithms to learn insights and recognize patterns from data. Essentially, ML takes an approach that allows the computer to learn to program itself through its own experience.
When a company employs AI programs, they use machine learning. ML begins with data, and AI still cannot paint like a human, even with substantial data sampling.
If you’re a traditional programmer, you know that programming usually involves writing detailed instructions that inform the computer exactly what to do. By contrast, there are some tasks that humans can perform quickly but computers may find challenging to execute, such as recognizing faces. Artificial intelligence is mostly capable of recognizing images and faces due to machine learning techniques.
That’s artificial intelligence and machine learning in a nutshell. Machine learning lets artificial intelligence try to resolve questions by providing it with lots of information to learn from. By running tests and practicing the most basic code, this requires massive computing power. That’s where Google Colab comes in.
Why Use Google Colab?
Google has made TensorFlow open source code and allowed public access to Google Colaboratory for free. It has enabled easier education about and development of machine learning applications, even for people that cannot afford costly computational infrastructure. The Google Colab workspace is installed via Google Workspace Marketplace, and everything you create and modify can be stored in Drive or GitHub. Your code executes in a virtual machine that is exclusive to your account.
Python and Jupyter can have intensive CPU and GPU workload requirements. Colab provides you free access to computing infrastructure enabling you to evaluate and execute your code. It comes with a free tier for amateurs, scholars, and small experimental projects, while data scientists and AI experts can subscribe to the company’s premium plans to gain access to higher memory machines, faster GPUs, and more compute units
What Can You Do in Google Colab?
As a programmer, there are numerous things you can do in Colab:
- Write, execute, and share code in Python
- Collaborate in real-time with your team
- Link with GitHub to publish or import notebooks
- Import external datasets
- Document code that comprises of mathematical equations
- Access GPU, and TPU runtimes to undertake your work
- Use pre-installed libraries like TensorFlow, Matplotlib, and PyTorch, among other ML libraries
- Integrate with GitHub
- Use version history akin to Google Docs
- Train models using images, audio, and text
- Analyze and visualize data
Google Colab vs. Jupyter Notebook
Google Colab is built on Jupyter Notebook, a fully open-source product also available for free. IPYNB format notebooks are usually used for data exploration, machine learning experimentation, documenting code examples, and creating tutorials. Essentially, you can do the same in Google Colab as you could do in Jupyter Notebooks. The key differences between the two are:
- Collaborative Tools: Google Colab offers several collaboration tools that traditional Jupyter Notebook lacks. Real-time collaboration, document sharing, and cloud storage with others are among the most essential collaborative tools.
- Software: Traditional Jupyter Notebook requires software installations on your local hardware, and you have to install and maintain your libraries. In contrast, Colab works entirely in your web browser, making it easier to use since it is software that you already have.
- Document Sharing: Colab Notebooks are shared and stored using Google Drive. Like Google Docs and Sheets, your notebooks auto-save periodically, come with version history and can be shared with others without the need for them to install any software.
- Computing Power: Jupyter Notebooks are stored on your local machine, and the code is executed using the hardware. Colab utilizes massive computing power and offers a high level of access to computing infrastructure.
Up Your ML Game with Google Colab
Artificial intelligence is changing numerous industries and has made significant strides in recent years. Learning how to use AI and ML tools may be beneficial to data scientists, researchers, or anyone seeking to learn about AI. With Google Colab and the Jupyter open-source code, you can begin exploring these tools from the comfort of your home at no cost.
You’ll need to know the basics of Python and be familiar with GitHub before you can begin coding in these environments. Colab is an excellent place to start learning since there are numerous guides and tutorials for the various things you can accomplish in Colab.
