MachineLearning

For a successful natural language processing project, collecting and preparing data, building resilient pipelines, and getting "model ready" can easily take months of effort even with the most talented engineers. But what if we could reduce the data required to a fraction? In this article, we’ll cover how transfer learning is making world-class models open source and introduce BERT (bidirectional encoder representations from transformers). BERT is the most powerful NLP “tool” to date. We’ll explore how it works and why it will 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 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.