MachineLearning

For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.

IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it’s possible to create a new, refined score based on IMDb information.

Supervised learning is the key to computer vision and deep learning. However, what happens when you don’t have access to large, human-labeled datasets?

In this article, Toptal Computer Vision Developer Urwa Muaz demonstrates the potential of semi-supervised image classification using unlabeled datasets.

Data collection and preparation slow down traditional NLP projects. However, transfer learning and BERT can reduce the amount of data required and change the way companies execute NLP projects.

The increasing accuracy of machine learning systems has resulted in a flood of applications using them. As machine learning models matured and improved, so did ways of attacking them.

In this article, Toptal Python Developer Pau Labarta Bajo examines the world of adversarial machine learning, explains how ML models can be attacked, and what you can do to safeguard them against attack.

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

In this article, Toptal AI expert Necati Demir, PhD, moves past the evil artificial intelligence of science fiction to discuss the current algorithm-based prediction AI, the economics of AI, and the economic benefits of artificial intelligence.

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.