Top Machine Learning Frameworks Compared: Scikit-Learn, Dlib, MLib, Tensorflow, and More
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Mateusz Opala
Machine learning is one of the hottest topics in modern software development. Still, it is a fairly young technology, which is undergoing rapid change and development. We wrote this article to aid you in selecting the right flavor of ML for your project, showcasing the top frameworks along with their upsides and shortcomings.
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React Native Ultimate Guide - Learning Resources and Courses, Tools, Open Source and More
Joanna Jasnowska
Since its first release in 2015, React Native has been gaining more and more popularity. Its community is growing, and a lots of people are starting to write mobile applications using RN - including some big players like Facebook & Instagram (obvious ones), Skype, Pinterest, Uber etc (find more here). We decided to gather the top resources dealing with React Native created by the vibrant community within the last few years and share them with you.
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The recent news about Serverless Inc., the company behind the Serverless Framework, getting 10M$ Series A funding assured many that serverless is still growing since the inception of the term around 2014. With many technologies, it is not only how well it works that makes developers use it. The confidence in the product is just as important.
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React Native still evokes many questions. Quora is a good place to find some answers concerning this topic. So taking into consideration our love for RN and our desire to help others, we decided to answer 10 most nagging questions. Here you can find small pieces of RN knowledge summarized in this handy post.
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Service objects in Rails will help you design clean and maintainable code. Here's how.
Tomek Pewiński
If you’re following the trends in Ruby on Rails, you’ve probably heard the word ‘service’ a few times, or perhaps even encountered it in code that lives in the app/services directory. In this post I’ll try to clarify what a service is, when it’s useful, and the different kinds of services I’ve used previously.
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How to map the energy infrastructure of a country, make it free and profit? The story of Petro Niche
Karolina Kurstak
There are few places on earth as rich in primary energy resources as Canada. The country’s oil reserves make up 10% of the world’s total (see Graph 1). Canada’s also the world’s fourth biggest natural gas producer (see Graph 2). The industry, conservative in its nature, has been slowly adapting to new technologies, especially regarding mobile solutions.
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How to Estimate the Development Cost of a Django Application? 5 Factors That Affect Python Developer Rate
Jakub Protasiewicz
Django is a popular back-end framework for building web apps with the Python language. It can be used to develop complex websites with vast databases, and it promotes fast software development that’s perfect for high-velocity web projects. Many companies chose Django for just these reasons, including Instagram, The Washington Times, and Mozilla. So what’s the secret to Django’s high development speed? Find out how to estimate development speed and lower the costs of building your Django app.
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Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.
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Startups planning for product development need to consider many things when it comes to picking the right programming language. Often working on a tight budget, startups must carefully consider many factors: the speed of development, the popularity of language, the cost of developers, libraries, integrations, security, scalability, and stability. It is for these reasons that your best bet will be to go for a robust and established technology such as Python.
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The new surge in Artificial Intelligence development has a strong impact on the InfoSec sector. However, there is much more nuance to it than you may think. Machine learning algorithms are not ready to take over on both sides of the fence. They are just a powerful tool that both security specialists and their adversaries use in their arms race.
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