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- Python Weekly (Issue 469 October 8 2020)
Python Weekly (Issue 469 October 8 2020)
Python Weekly - Issue 469
Python Weekly
Welcome to issue 469 of Python Weekly. Let's get straight to the links this week.
From Our Sponsor
Identify a misbehaving service within a distributed system by stitching together distributed trace data from interconnected services. Optimize the performance of your Python apps end-to-end with Datadog's newest feature, code-level profiling.
News
The pharmaceutical firm has revealed how it is using sophisticated machine-learning tools to speed up drug discovery.
The 3rd Annual Python Web Conference will be fully virtual and the audience will range from beginner to expert level attendees. The call for proposals is now open and they are looking for a wide variety of talk levels and subjects.
Articles, Tutorials and Talks
Recent research introduces a deep learning framework that allows to create a numerical representation of a voice from a few seconds of audio, and to use it to condition a text-to-speech model trained to generalize to new voices. This talk introduces how a complex deep learning process for cloning voices unseen during training can easily be converted to a Streamlit app using pre-trained models.
This article explains the new features in Python 3.9, compared to 3.8.
Using Python 3.6, OpenCV, Dlib and the face_recognition module.
Learn how to call Python code from Golang code using the Python C API.
Python deque is a double-ended queue. You can append to both ends and pop from both ends. The complexity of those operations amortizes to constant time. This post explains Python 3 internal implementation of deques. It uses a linked list of blocks of 64 pointers to objects. This reduces memory overhead since there are fewer previous and next links.
Stop spending time manually tweaking misaligned arrows.
Writing a Passable Programming Language in Half-a-Day.
And how to write if you have to.
Recipes for using and creating awesome Python context managers, that will make your code more readable, reliable and less error prone.
Build a U.S. Presidential Polling Dashboard that scrapes the latest polls from FiveThirtyEight with BeautifulSoup and renders the data in a Flask Dashboard!
When customers report their thorniest problems and all of your diagnostic tools have failed you, how can you help? In this article, we explore a technique and a tool for Django apps that can help you swoop in and save the day.
Interesting Projects, Tools and Libraries
Human-First AI solves the “cold-start” problem of Industrial AI: encoding human expertise to augment the lack of data, while bridging to powerful ML—based on experience building AI solutions at Panasonic: robotics predictive maintenance, cold-chain energy optimization, Gigafactory battery mfg, avionics, automotive cybersecurity, and more.
GHunt is an OSINT tool to extract information from any Google Account using an email.
An easy, flexible and extensionable GUI debugger.
Archai is a platform for Neural Network Search (NAS) that allow you to generate efficient deep networks for your applications.
A machine learning tool that allows to train, test and use models without writing code.
An efficient PyTorch library for deep generative modeling (StyleGANv1v2, PGGAN, etc)
Create recursive image rotation animations.
A 3270 font in a modern format.
Flask web app used to create Spotify playlists based on selected tracks and personal preferences.
AcurusTrack is a highly predictable multiple object tracker. It is based on a custom data association approach.
Network Exploitation, Reconnaissance & Vulnerability Engine.
Find functions when you can't remember their name.
New Releases
Python 3.9.0 is the newest major release of the Python and it contains many new features and optimizations.
Upcoming Events and Webinars
There will be following talks
Demystifying Flask's Application and Request Contexts with pytest
Building your first REST API with AWS’s Open Data Sets and Chalice
Strategies for testing Python code that uses Amazon S3
There will be following talks
Python for the Ethereum Blockchain
Hummingbot for crypto trading
Learn how to export monitoring metrics from your code and capture it in Prometheus. We will build upon a simple web application, using Python client APIs to track events in the code, while introducing the fundamentals of application monitoring and the Prometheus data-fetching model. You will learn how to add monitoring to your own applications, and how to query and interpret the resulting data. As a bonus we will also introduce you to building grafana dashboards with your data.
In this talk, Marc Garcia will give a quick overview of the ecosystem, analyze the different components of these systems, and discuss what the future dataframe ecosystem could look like.
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