Python Weekly (Issue 381 January 24 2019)

Python Weekly - Issue 381

January 24, 2019

Python Weekly

Welcome to issue 381 of Python Weekly. A lot of you have contacted me about not receiving newsletter last couple of weeks. I had a family emergency due to which I couldn't send newsletter. We are back to our regular schedule this week.

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Articles, Tutorials and Talks

The goal of this post is to build an intuition and understanding for how deep learning libraries work under the hood, specifically TensorFlow. To achieve this goal, we will mimic its API and implement its core building blocks from scratch. This has the neat little side effect that, by the end of this post, you will be able to use TensorFlow with confidence, because you’ll have a deep conceptual understanding of the inner workings. You will also gain further understanding of things like variables, tensors, sessions or operations.

Let's build a natural language processing (NLP) model that can help out your customer support agents by suggesting previously-asked, similar questions.

Microservice architectures are all the rage and new frameworks have emerged to build them.  In this episode Casey, Alan, and Tyrel round up some of the frameworks they've been exploring and how they stack up to one another.

Learn about powerful time series tools in the pandas library and get hands-on programming some time series analysis in Python in this data science tutorial.

Say you have an external hard drive with layers upon layers of cryptically named folders and intricate mazes of directories. How can you make sense of this mess? Python offers various tools in the Python standard library to deal with your file system and the folderstats module can be of additional help to gain insights into your file system. In this article, you will learn the various ways to traverse and explore the file system with Python.

Can we teach a neural net to convert face embedding vectors back to images?

In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction.

This post is an introduction to and practical use cases for The Web Application Messaging Protocol.

Python provides many advantages over the traditionally popular VBA scripts for finance professionals looking to automate and enhance their work processes. This article explores how to use Python and finance together via a practical step-by-step tutorial.

Since reader endpoints are only accessible as individual nodes in AWS ElastiCache, applications need to load balance read requests themselves. Read more to see how SendBird eliminates single points of failure in our Redis deployment by implementing an active health checker with HAProxy.

One of the most common hurdles with developing AI and deep learning models is to design data pipelines that can operate at scale and in real-time. Data scientists and engineers are often expected to learn, develop and maintain the infrastructure for their experiments, but the process takes time away from focussing on training and developing the models. But what if you could outsource all of the non-data science to someone else while still retaining control? In this article, you will explore how you can leverage Kubernetes, Tensorflow and Kubeflow to scale your models without having to worry about scaling the infrastructure.

Python Jobs of the Week

Farfetch relies heavily on insights gained from data to guide feature development and on metrics to detect new areas of improvement. This position will contribute to the engineering of the next-generation data science products and interfaces that connect our Machine Learning technology to the vast ecosystem of internal applications. The ideal candidate will be comfortable in a startup environment and will bring an energetic, fun and creative approach to their work.

Beeswax is an easy to use, massive scale and high availability advertising platform. At our transaction volumes we regularly deal with scaling challenges as our tech grows and evolves. To manage the firehose of data coming in, we explore complex tradeoffs and carefully architect high performance distributed systems. Those in turn require elegant and thoughtfully designed APIs to make the systems accessible to both our team and our customers. We are looking for someone with experience with software architecture, performance, scaling, data modelling, and API (or apiary) design.

Interesting Projects, Tools and Libraries

LASER is a library to calculate and use multilingual sentence embeddings. The toolkit now works with more than 90 languages, written in 28 different alphabets. LASER achieves these results by embedding all languages jointly in a single shared space (rather than having a separate model for each). 

An API Wrapper For Every API.

A fully configurable and extendable Bash obfuscation framework. This tool is intended to help both red team and blue team.

Notetaking app that can answer your questions and makes summaries for you.

Lightweight framework for fast prototyping and training deep neural networks in medical imaging.

Makes sending emails easy and DRY — For Python 3.

Websocket RPC and Pub/Sub for Python applications and microservices.

Desktop Backup Client based on BorgBackup.

Unofficial Instagram GraphQL API to collet data without authentication.

Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.

An eye friendly plugin that fades your inactive buffers and preserves your syntax highlighting!

New Releases

Upcoming Events and Webinars

There will be a talk, Using Pytest to Write Beautiful Tests and a Bulletproof Django Application.

During this meetup we will have a Kahoot based competition. The competition will take place in two parts. The first part is open for everyone. The second one will be open for those who did best in the first part.

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