#MachineLearning Posts

Manually gathering information about user-generated data is time-consuming, to say the least. That’s why more organizations are turning to automatic sentiment analysis methods—but basic models don’t always cut it. In this article, Toptal Freelance Data Scientist Rudolf Eremyan gives an overview of some sentiment analysis gotchas and what can be done to address them.

Will AI make improve our lives beyond comparison, or will it steal our jobs and destroy humanity?

In this article, Dr. Necati Demir, Toptal Freelance AI Expert, discusses not the evil artificial general intelligence of science fiction but the current algorithm-based prediction AI and discusses how the economics of AI work, along with their effects on businesses.

Leveraging DeepMind’s breakthrough AI approaches takes some work, but the results are astounding. In this article, Toptal Freelance Deep Learning Engineer Neven Pičuljan guides us through the building blocks of reinforcement learning, training a neural network to play Flappy Bird using the PyTorch framework.

Employing Python to make machine learning predictions can be a daunting task, especially if your goal is to create a real-time solution. However, Tensorflow and Scikit-Learn can significantly speed up implementation.

In this article, Toptal Python Developer Guillaume Ferry outlines a simple architecture that should help you progress from a basic proof of concept to a minimal viable product without much hassle.

How do you approach machine learning problems? Are neural networks the answer to nearly every challenge you may encounter?

In this article, Toptal Freelance Python Developer Peter Hussami explains the basic approach to machine learning problems and points out where neural may fall short.

Machine learning and artificial intelligence are popular topics, vast domains with multiple paradigms to solve any given challenge.

In this article, Toptal Machine Learning Expert Adam Stelmaszczyk walks us through implementing deep Q-learning, a fundamental algorithm in the AI/ML world, with modern libraries such as TensorFlow, TensorBoard, Keras, and OpenAI Gym.

Deep learning has proven its power across many domains, from beating humans at complex board games to synthesizing music. It has also been used extensively in natural language processing.

In this article, Toptal Freelance Software Engineer Shanglun (Sean) Wang shows how easy it is to build a text classification program using different techniques and how well they perform against each other.

Machine learning, combined with some standard image processing techniques, can result in powerful video analysis tools.

In this article, Toptal Freelance Software Engineer Michael Karchevsky walks through a solution for a machine learning competition that identifies the species and lengths of any fish present in a given video segment.

While machine learning sounds highly technical, an introduction to the statistical methods involved quickly brings it within reach. In this article, Toptal Freelance Software Engineer Vladyslav Millier explores basic supervised machine learning algorithms and scikit-learn, using them to predict survival rates for Titanic passengers.

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.

More than 60 percent of trading activities with different assets rely on automated trading and machine learning instead of human traders. Today, specialized programs based on particular algorithms and learned patterns automatically buy and sell assets in various markets, with a goal to achieve a positive return in the long run.

In this article, Toptal Freelance Data Scientist Andrea Nalon explains how to predict, using machine learning and Python, which trade should be made next on the S&P 500 to get a positive gain.

Clustering algorithms are very important to unsupervised learning and are key elements of machine learning in general. These algorithms give meaning to data that are not labelled and help find structure in chaos. But not all clustering algorithms are created equal; each has its own pros and cons.

In this article, Toptal Freelance Software Engineer Lovro Iliassich explores a heap of clustering algorithms, from the well known K-Means algorithm to the elegant, state-of-the-art Affinity Propagation technique.

Today, a massive amount of data is available in the form of networks or graphs. For example, the World Wide Web, with its web pages and hyperlinks, social networks, semantic networks, biological networks, citation networks for scientific literature, and so on.

A tree is a special type of graph, and is naturally suited to represent many types of data. The analysis of trees is an important field in computer and data science. In this article, we will look at the analysis of the link structure in trees. In particular, we will focus on tree kernels, a method for comparing tree graphs to each other, allowing us to get quantifiable measurements of their similarities or differences. This an important process for many modern applications such as classification and data analysis.

Machine Learning, in computing, is where art meets science. Perfecting a machine learning tool is a lot about understanding data and choosing the right algorithm. But why choose one algorithm when you can choose many and make them all work to achieve one thing: improved results.

In this article, Toptal Engineer Necati Demir walks us through some elegant techniques of ensemble methods where a combination of data splits and multiple algorithms is used to produce machine learning results with higher accuracy.

Detecting objects of interest in images has always been an interesting challenge in the realm of computer vision, and many approaches have been developed over recent years. As mobile platforms are becoming increasingly powerful, now is the perfect opportunity to develop interesting mobile applications that take advantages of these algorithms. This article walks us through the process of building a simple iOS application for detecting objects in images.

Machine learning has changed the way we deal with data. Data driven problems, that are difficult to solve using standard methods, can often be tackled with much more ease using machine learning algorithms. In this article, we will explore Azure Machine Learning features and capabilities through solving one of the problems that we face in our everyday lives.

This Machine Learning tutorial introduces the basics of ML theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.

The recent resurgence in Artificial Intelligence has been powered in no small part by a new trend in machine learning, known as “Deep Learning”. In this article, I’ll introduce you to the key concepts and algorithms behind Deep Learning, beginning with the simplest building block.