Python Hub Weekly Digest for 2021-10-17

AI as an API - Part 1
Learn how to turn a ML Trained Model into an REST API using Keras, FastAPI & NoSQL.

Python 3.10.0
Python 3.10.0 is the newest major release of the Python programming language, and it contains many new features and optimizations.

Mastering Structural Pattern Matching
Learn how to use Python 3.10’s new Structural Pattern Matching feature, and why Python would suddenly adopt a complex feature usually reserved for functional programming languages.

📖 Articles

How SQL Injection attack works
A funny story where the author found a SQL Injection vulnerability in an IBM service, also how it works, how to explore it using Python and Flask.

Create a Stunning PDF Flyer in Python with borb
In this guide, we'll create a beautiful stylized PDF flyer using nothing but Python and borb - a pure-Python library for working with PDF documents.

7 Ways to Make Your Python Project Structure More Elegant
Here are the best practices for a manageable, scalable, and easily understandable python project ...

Python 3.11: “Zero cost” exception handling

Essential Linux Command-Line Tricks for Computer Vision Researchers
In this post, we go through some useful command-line tricks which can help Computer Vision ...

Python leads Tiobe Index for first time in 20 years

Looking at notebooks from a new perspective
How to better communicate with Jupyter notebooks.

Analysis of Runing Activities from Garmin Watch Using Python
Analysis of Running Activities from Garmin Watch Using PythonDiscover your running patternsDuring ...

What's great about Python 3.10?
What changed in Python 3.10 and which of those changes matter for you? I’ve spent this week ...

Streamlit 1.0
Streamlit used to be the simplest way to write data apps. Now it's the most powerful.

How Dialpad Moved Its Python AI Development from Pip to Poetry
Learn how DialPad switched from Python's pip to Poetry for next generation package management.

Python and Lua (2019)

Why you can’t switch to Python 3.10 just yet
Python 3.10 is out now — when should you start using it?

OpenCV Contour Approximation
In this tutorial, we’ll learn about a step-by-step implementation and utilization of OpenCV’s ...

ThoughtfulDev / EagleEye
Stalk your Friends. Find their Instagram, FB and Twitter Profiles using Image Recognition and Reverse Image Search.

Nowcasting the Next Hour of Rain
DeepMind's latest research and state-of-the-art model advances the science of Precipitation Nowcasting. You can also explore the data they used for training and find a pre-trained model for the UK via

Building a Sentiment Analysis model using Yelp Reviews and ML Ensemble Methods
What is actually the difference between a three-star review and a four-star review? Natural ...

A new AI constraint solver for Python: OptaPy
Python developers can now solve AI planning problems (such as the vehicle routing problem and employee rostering) with OptaPy. This post shows you how to use OptaPy and a bit of plain Python code to tackle a typical mathematical optimization problem: generate a better school timetable schedule for teachers and students.

End-to-end tinyML audio classification with the Raspberry Pi RP2040
In this article, we will demonstrate how an Arm Cortex-M based microcontroller can be used for local on-device ML to detect audio events from its surrounding environment. This is a tutorial-style article, and we’ll guide you through training a TensorFlow based audio classification model to detect a fire alarm sound.

PostgreSQL vs Python for data evaluation: what, why, and how
Get a primer on using PostgreSQL to more efficiently perform your data evaluation tasks - done in Excel, R, or Python. Complete with short SQL refresher section, along with 1-to-1 code snippets comparing TimescaleDB and PostgreSQL code against Python code.

TensorFlow for Computer Vision - Full Tutorial for Beginners
Learn how to use TensorFlow 2 for computer vision in this complete course. The course shows you how to create two computer vision projects. The first involves an image classification model with a prepared dataset. The second is a more real-world problem where you will have to clean and prepare a dataset before using it.