Fashion has always been at the forefront of innovation from the invention of the sewing machine to the rise of e-commerce. Like tech, fashion is forward-looking and cyclical. Fashion technology is defined as any cutting-edge technology that develops cutting-edge tools for the fashion industry, whether to boost production or consumption. …

The majority of machine learning models rely on human-created data. But the interaction between humans and machines does not end there; the most powerful systems are designed to allow both sides to interact continuously via a mechanism known as “Human in the loop” (HITL). HUMAN-IN-THE-LOOP (HITL) machine learning necessitates human…

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. …

Human pose estimation and tracking is a computer vision task that includes detecting, associating, and tracking semantic key points. Examples of semantic key points are “right shoulders,” “left knees,” or the “left brake lights of vehicles.” The performance of semantic keypoint tracking in live video footage requires high computational resources…

Robotic systems have proven to be incredibly adept at selecting, organizing, arranging, and cataloging products on the production line or in retail settings. The field of robotics is expanding its permitted operating space. Robots can now work more quickly on the assembly line and in brand-new settings like supermarkets, hospitals…

When given enough training data, machine learning algorithms can do amazing feats. Unfortunately, many applications still struggle to access high-quality data. Making copies of current data and making small modifications to them is one method for increasing the diversity of the training dataset. This is referred to as “data augmentation.”…

The quality and volume of data are critical to the success of AI algorithms. Real-world data collection is expensive and time-consuming. Furthermore, due to privacy regulations, real-world data cannot be used for research or training in most situations, such as healthcare and the financial sector. Another disadvantage is the data’s…

Autonomous vehicles are still working towards reaching the stage of full autonomy. A fully functioning and safe autonomous vehicle must be competent in a wide range of machine learning processes before it can be trusted to drive on its own. From processing visual data in real-time to safely coordinating with…

NLP (natural language processing) and document AI are technologies that are quickly developing and have a wide range of prospective applications. In recent years, the usage of NLP and document AI has significantly increased across a variety of industries, including marketing, healthcare, and finance. These solutions are being used to…