Techno Blender
Digitally Yours.
Browsing Tag

Dimitris

GPT-4 is Here: Is It Really Changing the Game for Language AI? | by Dimitris Poulopoulos | Mar, 2023

OpinionIs GPT-4 the next big step in AI we were all waiting for?Image generated by Stable DiffusionToday, I woke up, and the hashtag #gpt-4 was trending on Twitter. Immediately, I thought that OpenAI released the next version of the most popular language model, and people were going mad about it. So, I started reading the top threads on this topic. To my disappointment, most of them were rumors about the release date, how big GPT-4 will be, and what it will be capable of, while the rest were memes from various Sci-Fi…

The Perfect Text Editor for Jupyter: A Complete Python IDE | by Dimitris Poulopoulos | Mar, 2023

From syntax highlighting to code completion, a complete Python IDE inside JupyterPhoto by Max Duzij on UnsplashThis article is part of a series. Check out the full series: Part I, Part II, Part III.Over the past few days, we’ve been building a complete Python IDE inside Jupyter. In this article, we will add the final touches and package everything in a Docker image to create a portable working environment for data scientists and Machine Learning engineers.Jupyter is not exactly an IDE. It’s not even an IPython UI, as many…

Jupyter Already Has a Perfect Text Editor: Building a Python IDE | by Dimitris Poulopoulos | Mar, 2023

Make Jupyter your all-in-one IDE and elevate your Python development experiencePhoto by Fotis Fotopoulos on UnsplashThis article is part of a series. Check out the full series: Part I, Part II.In previous parts of this series, we covered why many developers do not consider Jupyter as a fully integrated development environment and how the lack of a powerful text editor is one of the main reasons.Thus, we decided to look at JupyterLab as a platform that we could use to create our own Python IDE from scratch. Extending the…

Jupyter Already Has a Perfect Text Editor: This is How You Can Configure It | by Dimitris Poulopoulos | Mar, 2023

How to get a VS Code-like experience in Jupyter with a great text editorPhoto by Douglas Lopes on UnsplashOur previous article stated that many engineers do not consider JupyterLab a complete IDE. One of the main reasons is that JupyterLab does not have a powerful text editor like VS Code or Sublime Text.JupyterLab allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It’s the perfect tool for this kind of interactive work. However, the truth is that its text…

Unlocking the Potential of JupyterLab: Discover the Powerful Text Editor You Never Knew You Had | by Dimitris Poulopoulos | Mar, 2023

Unleash Your Coding Efficiency and Productivity in JupyterLab with a GreatText EditorPhoto by Luca Bravo on UnsplashJupyterLab is an open-source web-based interactive coding environment that many would argue is not a complete IDE. One of the many reasons that support this is that JupyterLab does not have a powerful text editor.JupyterLab allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It provides a flexible and powerful platform for scientific computing,…

Movie Recommendations with Neo4j. Building a simple movie recommender… | by Dimitris Panagopoulos | Feb, 2023

Building a simple movie recommender with Python and Neo4jImage created by author using stable diffusion and code described in https://bytexd.com/get-started-with-stable-diffusion-google-colab-for-ai-generated-art/Creating recommendations is a common use case of machine learning. In this post, we will demonstrate how to use a graph database to create a simple movie recommendation system. The proposed methods are not state-of-the-art. But using graph databases is easy to implement and easy to explain. They could form the…

Mastering Containerization: A Guide to Creating Docker-Like Environments without Docker | by Dimitris Poulopoulos | Feb, 2023

Unlocking the power of containerization: A step-by-step tutorial on building container-like environments.Photo by Timelab Pro on UnsplashContainers have revolutionized how we deploy and manage applications, providing unparalleled levels of portability, scalability, and consistency.However, you shouldn’t be intimidated by Docker’s sleek exterior — it’s time to delve into the mechanics that make containerization possible. By understanding the inner workings of Docker, you’ll gain a deeper appreciation for the technology and…

The Power of Linux Cgroups: How Containers Take Control of Their Resources | by Dimitris Poulopoulos | Jan, 2023

Optimizing Container Resource Allocation with Linux Control GroupsPhoto by Joshua Hoehne on UnsplashThe last article examined how to use Linux Namespaces to create isolated environments within a single Linux system. This article is part of our effort to deeply understand how containers work by looking under the hood.Namespaces are the first step of our journey. We saw how you can create a PID namespace to create a world where the processes running within are under the assumption that they are the only ones in existence,…

Containers: How They Work Under the Hood and Why They’re Taking Over the Data Science World | by Dimitris Poulopoulos | Jan, 2023

A Beginner’s Guide to Understanding the Magic of DockerContainerized city — Image generated by Stable DiffusionDocker has taken the world by storm, and for a good reason. Lightweight, portable containers make it easy to package and deploy applications, ensuring they run consistently and reliably on any platform.But what exactly are Docker containers, and how do they work under the hood? A Google search will give you hundreds of articles about how containers compare to Virtual Machines (VMs), but this does not answer the…

Transfer Learning: The Secret Weapon for Boosting Your ML Models | by Dimitris Poulopoulos | Dec, 2022

Unleashing the Power of Pretrained Models for Improved Performance and Faster Training TimesImage generated by Stable DiffusionThe development of Machine Learning models involves training algorithms on large sets of (labeled) data, which can be time-consuming and resource-intensive. Thus, we need techniques, such as distributed training or transfer learning, that allow us to iterate faster and can reduce the time from research to market.Transfer learning is a powerful technique in machine learning that allows you to…