Python

TensorFlow is more than just a machine intelligence framework. It is packed with features and tools that make developing and debugging machine learning systems easier than ever. In this article, Toptal Freelance Software Engineer Dino Causevic gives us an overview of TensorFlow and some auxiliary libraries to debug, visualize, and tweak the models created with it.

TensorFlow makes implementing deep learning on a production scale a breeze. However, understanding its core mechanisms and how dataflow graphs work is an essential step in leveraging the tool’s power. In this article, Toptal Freelance Software Engineer Oliver Holloway demonstrates how TensorFlow works by first solving a general numerical problem and then a deep learning problem.

Generative adversarial networks, among the most important machine learning breakthroughs of recent times, allow you to generate useful data from random noise. Instead of training one neural network with millions of data points, you let two neural networks contest with each other to figure things out. In this article, Toptal Freelance Software Engineer Cody Nash gives us an overview of how GANs work and how this class of machine learning algorithms can be used to generate data in data-limited situations.

Donald Knuth said that "premature optimization is the root of all evil." But there comes a time, usually in mature projects with high loads, when the need to optimize presents itself. In this article, Toptal Freelance Software Engineer Iulian Gulea talks about five common methods to optimize a web project’s code using principles that can be used in Django as well as other frameworks and languages. Using these principles, he demonstrates how to reduce the response time of a query from 77 to 3.7 seconds.

The Hungarian graph algorithm solves the linear assignment problem in polynomial time. By modeling resources (e.g., contractors and available contracts) as a graph, the Hungarian algorithm can be used to efficiently determine an optimum way of allocating resources.

In this article, I will try to give you a good understanding of which scenarios could be covered by Celery. Not only will you see interesting examples, but will also learn how to apply Celery with real world tasks such as background mailing, report generation, logging and error reporting. I will share my own way of testing tasks beyond emulation and explain a few tricks that go beyond the official documentation and took me hours of research to discover myself.

Build a bot that analyzes the sentiment of incoming email messages using Recursive Neural Tensor Networks from the Stanford NLP library.

Twitter is a goldmine of data. Unlike other social platforms, almost every user’s tweets are completely public and pullable. In this tutorial, Toptal Freelance Software Engineer Anthony Sistilli will be exploring how you can use Python, the Twitter API, and data mining techniques to gather useful data.

Social networks are among the biggest sources of data today, and this means they are an extremely valuable asset for marketers, big data specialists, and even individual users like journalists and other professionals. Harnessing the potential of real-time Twitter data is also useful in many time-sensitive business processes. In this article, Toptal Freelance Software Engineer Hanee' Medhat explains how you can build a simple Python application to leverage the power of Apache Spark, and then use it to read and process tweets to identify trending hashtags.