Lionbridge AI has assembled a wealth of resources for machine learning and natural language processing activities. In our previous articles, we explained why datasets are such an integral part of machine learning and natural language processing. Without training datasets, machine-learning algorithms would have no way of learning how to do text mining, text classification, or categorize products.
Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects.
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone–especially your non-technical colleagues–to take.
Mainly Computer Vision (tasks/datasets/papers with code)
The artificial intelligence sector sees over 14,000 papers published each year. This field attracts one of the most productive research groups globally.
AI conferences like NeurIPS, ICML, ICLR, ACL and MLDS, among others, attract scores of interesting papers every year. The year 2019 saw an increase in the number of submissions.
Computer vision models have learned to identify objects in photos so accurately that some can outperform humans on some datasets. But when those same object detectors are turned loose in the real world, their performance noticeably drops, creating reliability concerns for self-driving cars and other safety-critical systems that use machine vision.