Radek Fabisiak was with the computers from his early days, remembers an orange screen with Win32, big floppy disks, and the sound of dial-up connecting to the internet.
He has got experience in full-stack development by working for top IT companies like Microsoft.
In the last years, in the core team of the Duomly, where as an addition to IT has got skills related to Online Marketing, SEO, Content Creation or building Online Business, now passing this knowledge to the Duomly’s audience.
He is a fan of skiing, video games, cooking, and martial arts in private life.
Choosing an integrated development environment (IDE) that suits your needs is often a non-trivial task. There are many available options. There is a number of interesting IDEs, with all kind of tools that might help you code faster and avoid some errors. Some of them are free and open-source.
Most well-known IDEs have support for Python, one of the most popular programming languages. They usually support linting, auto-completion, and choosing a Python environment for each project.
Hello everyone,
we came up with an idea to show you step by step what to do to become a developer. For this, we created a YouTube channel called *How to code with Duomly.* Every week we publish a new episode preparing you to be a programmer.
In every episode, we describe a different topic, like tools, software, programming languages, etc. For more information check out our first video!
Loops play an essential role in software development, and they are used to iterate through the arrays or other elements that are iterable. In Javascript, we have a few different types of loops. It sometimes can be confusing which loop we should use in a particular case and which one will be the best in case of our performance. In this article, I’m going to compare the loops in Javascript, describe their pros and cons, test the speed of each of them.
One of the main characteristics of the early 21st century is an outstanding rise in the amount of available data. This is followed by the significant improvements in computational power, storage capacities, as well as the improvements of the algorithms and software for data processing, interpretations, and predictions.
The skills related to data analytics, data science, machine learning, and artificial intelligence are widely demanded and well appreciated. Acquiring such skills requires a significant effort and months or years of learning.
You can download pdf version here.
Intro to machine learning interview questions Machine learning (ML) is a rising field. It offers many interesting and well-paid jobs and opportunities. To start working in machine learning, you should become familiar with:
mathematical fundamentals — linear algebra, calculus, optimization, probability, and statistics, etc.,
machine learning fundamentals — prepare data, validate and improve results, interpret results, recognize and avoid overfitting, etc.,
Python is a popular, general-purpose, and widely used programming language. It’s used for data science and machine learning, scientific computing in many areas, back-end Web development, mobile and desktop applications, and so on. Many well-known companies use Python: Google, Dropbox, Facebook, Mozilla, IBM, Quora, Amazon, Spotify, NASA, Netflix, Reddit, and many more.
Python is free and open-source, as well as most of the products related to it. Also, it has a large, dedicated, and friendly community of programmers and other users.
Intro to Machine learning libraries for Javascript Usually, people apply machine learning (ML) methods and algorithms using one of two programming languages: Python or R. Books, courses, and tutorials about Machine Learning most often use one of these languages as well (or both).
Python is a general-purpose programming language used not only for Machine Learning but for also for scientific computing, back-end Web development, desktop applications, etc. R is created primarily for statisticians.
Natural language processing (NLP) is one of the most promising fields of artificial intelligence that uses natural languages to enable human interactions with machines. There are two main approaches to NLP: – rule-based methods, – statistical methods, i.e., methods related to machine learning.
There are several exciting Python libraries for NLP, such as Natural Language Toolkit (NLTK), spaCy, TextBlob, etc.
A chatbot is a computer software able to interact with humans using a natural language.
Image recognition is one of the most widespread machine learning classes of problems. It aims at training machines to recognize images similarly as people do.
Image recognition belongs to the group of supervised learning problems, i.e., classification problems, to be more precise.
This article presents a relatively simple approach of training a neural network to recognize digits. This approach uses an ordinary feedforward neural network. The accuracy of the model can be further improved using other techniques.
Artificial intelligence (AI) is everywhere around us: image recognition and contacts suggestions on Facebook or Twitter, self-driving cars, software that beats champions in chess and go, books recommendations on Amazon, Google search, Apple Siri and Microsoft Cortana, and so on. It became a very important aspect of our lives. Its importance raises, and we rely on AI more and more every day hoping that it’s going to improve our well-being.