DATA BLOG

Google Colab is a powerful platform for writing and executing Python in your browser with zero configuration required, free access to GPU, and easy sharing, learning and quickly developing machine learning models in Python. It is based on the Jupyter notebook and supports collaborator development.

Google Collab allows developers to write and execute arbitrary Python code through the browser, document the code that supports mathematical equations, create, upload and share notebook from and to Google Drive, import and publish notebooks from GitHub, import external datasets from Kaggle. It also allows to integrate and support ML libraries such as PyTorch, TensorFlow, Keras, OpenCV, and finally, free cloud service with free GPU (graphical processing unit) and TPU (tensor processing unit) acceleration.

Python is one of the most popular programming languages in the world. With its simple syntax, readable code, and rich libraries, Python has become an essential tool for developers, data scientists, and machine learning experts.

The books listed here cover the fundamentals of Python and delve into specialized areas like data science and automation. Whether you are new to programming or seeking to broaden your knowledge, these books are excellent resources for your learning journey.

NVIDIA® Jetson Xavier™ NX brings supercomputer performance to the edge in a small form factor system-on-module (SOM). Up to 21 TOPS of accelerated computing delivers the horsepower to run modern neural networks in parallel and process data from multiple high-resolution sensors — a requirement for full AI systems.

Cloud Native

Jetson Xavier NX now features cloud-native support that lets developers build and deploy high-quality, software-defined features on embedded and edge devices. Pre-trained AI models from NVIDIA NGC and the NVIDIA Transfer Learning Toolkit give you a faster path to trained and optimized AI networks, while containerized deployment to Jetson devices allows flexible and seamless updates. Jetson Xavier NX accelerates the NVIDIA software stack with more than 10X the performance of its widely adopted predecessor, Jetson TX2.

Chinese Artificial Intelligence (AI) start-up Megvii Technology Limited announced that it makes its deep learning framework open-source, as China steps up the development of home-grown AI and makes the technologies more accessible to reduce reliance on US platforms.

Facebook data science groups offer a space for professionals and enthusiasts to share insights, learn new skills, and stay updated on industry trends. This article explores how these groups can help you connect with like-minded individuals, exchange knowledge, and access valuable resources. Whether you’re a beginner or an experienced data scientist, these communities provide a platform for growth and collaboration.

Data Science Community

Data Science is still a rapidly growing field that combines the power of statistical and computational methods to extract meaningful insights from large and complex data sets. It involves the use of various tools and techniques such as machine learning, data mining, and big data technologies to analyze data and extract information that can be used to improve business processes, optimize workflows and make data-driven decisions.

As soon as you start your server, it immediately jumps into the ocean of botnets trying to login with common credentials. And if your server has anything useful, sooner or later it may become a target for script kiddies or even more advanced folks with powerful computers and latest technologies, including Artificial Intelligence.

You can save your server from the botnets and somewhat complicate life for hackers using Fail2ban.

What is Fail2ban (F2B)

Fail2Ban is an IPF - Intrusion Prevention Framework that protects servers from external attacks. It was created by Cyril Jaquier (Switzerland) back in 2004 and since then it is under active development by the growing community.

📌 Updated Guide Available! This article was originally published in 2020. For current installation methods, modern frameworks like Starship, and 2025 plugin recommendations, see our updated guide: The Modern ZSH Guide: Everything You Need to Get Started.

Zsh is a powerful shell that enhances your command-line experience, and there are plenty of free resources available to help you master it. This article highlights some of the best tutorials, guides, and tools for learning Zsh, whether you’re a beginner or looking to expand your skills. From configuring plugins to customizing your shell environment, these resources provide a solid foundation for anyone wanting to dive deeper into Zsh.

This article reviews some of the best online courses for learning TensorFlow and PyTorch, two leading frameworks in the field of deep learning. It highlights key features, course content, and the skills you can expect to gain from each program. Whether you’re a beginner or looking to deepen your expertise, these courses provide valuable resources to help you excel in machine learning and artificial intelligence.

Intro

TensorFlow and PyTorch are both popular deep learning frameworks, and both have their own strengths and weaknesses. TensorFlow is widely adopted and has a large community, while PyTorch has been growing in popularity and is known for its ease of use and flexibility.

Sometimes, to increase the security of your server or VPS (Virtual Private Server), you need to create a block list for certain IP addresses.

Let’s say you found such a source of IP addresses to block. Say, from a certain country or region. Now you need to merge them all into one list for blocking on the server. How do you find and select all the IP addresses in the text?

You should always think about security. Especially when you’re writing code. And twice as much when you use other people’s code. It saves a lot of time and effort. But it can also lead to problems, especially if you don’t at least use common sense.

Modules

When programming, you can save commonly used classes, objects, and functions in modules. This is the official name for files with the extension .py. Conventionally, a module can be distinguished from an ordinary Python program by the fact that the program is designed to run and execute, while the module is designed to store frequently used constructs and import them into other programs.