Python Weekly (Issue 415 September 19 2019)

Python Weekly - Issue 415

September 19, 2019

Python Weekly

Welcome to issue 415 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

Monitor your Python environment with Datadog. Trace the path of distributed requests with flame graphs and automatically correlate logs and traces with individual requests to quickly troubleshoot your applications.

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News

PyCon 2020’s Call for Proposals has officially opened for talks, tutorials, posters, education summit, and charlas. We need beginner, intermediate, and advanced proposals on all sorts of topics as well as beginner, intermediate, and advanced speakers to present talks.

A persistent data structure is defined as a data structure that preserves the previous version of the data when the data is modified. Such data structures are effectively immutable, as operations on them do not update the structure in-place, but instead always yield a new updated structure. This PEP proposes to add a new fully persistent and immutable mapping type called frozenmap to the collections module.

Articles, Tutorials and Talks

Python’s reference implementation, CPython, makes a concerted effort to minimize complexity. While this has some consequences around performance of the language and short-term feature development, it means that CPython’s source code is surprisingly approachable for a language of its size and stature. By pulling apart tokenization, the first stage in the execution of any Python program, I hope to show just how approachable CPython’s internals are

In this intermediate-level tutorial we will use protein sequence embeddings as features for machine learning algorithms to improve their performance.

How much have I spent on Amazon? That's a scary question, but if you want to know the answer, here's how you can find it...and a lot more!

We will learn about attention mechanisms, how they are applied for text recognition using tensorflow attention ocr to extract numbers from car number plate.

This post is about how we use types to document and enforce a contract for our Python HTTP APIs. In the next few weeks, we’ll share details on more tools and techniques we’ve developed to manage our codebase’s quality.

This article explains how to develop chatbots with DeepPavlov, and why TensorFlow is an indispensable tool in doing so.

In this post, we explore a functional paradigm for implementing reinforcement learning (RL) algorithms. The paradigm will be that developers write the numerics of their algorithm as independent, pure functions, and then use a library to compile them into policies that can be trained at scale. We share how these ideas were implemented in RLlib’s policy builder API, eliminating thousands of lines of “glue” code and bringing support for Keras and TensorFlow 2.0.

Are you using Python and thinking about containers to deploy your app? Before you jump to Docker, consider other package formats that may fit better. This article explores one such format from LinkedIn.

And How We Eventually Made It 4 Times Faster.

Interesting Projects, Tools and Libraries

A Generative Adversarial Network for Face Anonymization.

Codimension is a free experimental Python 3 IDE, written mostly in Python with a couple of custom modules written in C/C++.

Learn How to Code in Python in 12 Weeks By Building Fun Real World Projects.

Neural Modules: a toolkit for conversational AI.

Collection of awesome Python types, stubs, plugins, and tools to work with them.

hypothesis-auto is an extension for the Hypothesis project that enables fully automatic tests for type annotated functions.

Lightweight package to query popular search engines and scrape for result titles, links and descriptions.

PyTorch Meta-learning Framework for Researchers.

Complete C99 parser in pure Python.

Conditional Transformer Language Model for Controllable Generation.

Bake — the strangely familiar task runner.

Small Python library that makes it easy to exploit race conditions in web apps with Requests.

Python 3.7 program to search Library Genesis via ISBN and fetch direct download links.

Upcoming Events and Webinars

There will be following talks

  • Django vs Rails: A detailed comparison

  • Building mapping applications with Django

There will be a talk, The Snake in Your Data: How Python is Used Today by Data Science Teams.

There will be a talk, Machine Learning with Splunk.

There will be following talks

  • itertools.groupby

  • Create narrowband filter with SciPy

There will be a talk, Deep Processing of Ridiculously Large Images.

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