Building a machine learning model consists of 7 steps, starting with problem identification and dataset creation.

MobileNet, introduced in 2017 by a team of researchers at Google, is a Deep Learning model for Smartphones, IoT, and embedded devices.

Stanford University researchers, along with industry experts, delve into the evolving landscape of AI and home robotics.

AlexNet is a Image Classification model released in 2012 and the first model to use CNN based Deep Neural Network.

We introduce the best lightweight computer vision models for fast production, accurate detection, and ease of use.

U-Net is an image segmentation model that features a U-shaped architecture, comprising two main parts: an encoder and decoder.

RetinaNet is a single-stage object detector that uses Focal Loss and two task-specific sub-networks for object detection.

Explore the decision tree algorithm and enhance your Python skills with step-by-step instructions in this comprehensive guide.

Object and image localization in computer vision: enabling machines to detect and accurately pinpoint objects in images.

Model drift can degrade a model's performance. Learn about the differences between concept drift vs data drift and mitigation strategies.

Gradient descent is based on a gradual, iterative approach to solving the problem of a function's minimization.

Knowledge Graph is a knowledge base that uses graph data structure to store and operate on the data. It powers applications such as Google