Google has come up with a framework for data collection inspired by software development concepts in a 5-step cyclical process. In this article, we will examine google’s proposed data collection framework.
In this article, we will introduce Tensorboard and explain how it can be used on AIEX.
The majority of machine learning algorithms work by minimizing or maximizing an ‘objective function’. Loss Functions are a group of objective functions that are supposed to be minimized. These functions are sometimes referred to as “cost functions” in artificial intelligence. Using the loss function, we can evaluate the ability of the model to predict new values.
“Transfer learning” is a machine learning concept that involves storing machine knowledge and using it on different, related problems. For example, the knowledge gained from learning to recognize cars (from car images or videos) can also be applied to bus recognition problems.
Computer vision (CV) in logistics can promote the ability of manufacturers and managers to detect problems and optimize processes. The company owners can take advantage of artificial intelligence (AI) in logistics to enhance efficiency and effectiveness by accelerating manual processes as well as improving safety while significantly reducing operational costs.
A GAN (Generative Adversarial Network) is a framework to estimate generative models. This framework includes two models: generators and discriminators that are utilized to discover and learn the patterns in input data.
This article introduces the concept of Neural Networks in detail. We will compare neurons in human brains with artificial neurons and explain the underlying mathematical model.
This article will explain Machine Learning concepts in detail through real-world examples. We will then go over various types of learning in ML.