AI With Python – Computer Vision

AI With Python – Natural Language Processing (NLP)

Computer Vision is a multidisciplinary field of artificial intelligence that enables machines to interpret, analyze and understand visual information from the world, much like the human visual system. It involves developing algorithms and systems that enable computers to gain insights from images, videos, and other visual data, allowing them to recognize objects, understand scenes, and perform tasks such as image classification, object detection, and facial recognition.

Image Processing and Transformation

Image processing and transformation refer to the techniques and methods used to manipulate and enhance digital images. These processes involve applying various operations to modify the appearance, quality, or information content of an image. Here are key concepts related to image processing and transformation:

Image Recognition Architectures

Image recognition architectures are specialized models or neural network structures created for the purpose of identifying and categorizing objects within images. Throughout time, numerous architectures have been discovered. Some of the image recognition models are listed down below:

Object Detection Architectures

Object detection architectures leverage deep learning techniques to detect and classify objects with varying orientations. There are two main types for object detection techniques two-stage detectors and single shot detectors.

Two-stage Detectors

Two-stage detectors follow a two-step process. First, they generate region proposals that are likely to contain objects using methods like region proposal networks (RPNs). In the second step, these proposals are classified and refined to obtain the final object detections. Some of the two stage detection models are:

R-CNN (Region-based Convolutional Neural network)
Fast R-CNN
Faster R-CNN
Cascade R-CNN

Single Shot Detectors

Single Shot Detectors perform object detection in a single forward pass through the network. They predict bounding boxes and class probabilities directly from predefined anchor boxes across multiple scales. Example models include:

Image Segmentation Architectures

Image segmentation architecture models to create partition an input image into distinct regions or objects. Each pixel in the image is labeled, assigning it to a particular segment. The main architectures of image segmentation include:

U-Net
K means clustering
Mask R-CNN
YOLOv8
Cascade Mask R-CNN
PSPNet (Pyramid Scene Parsing Network)

Computer vision plays a crucial role in various applications, including autonomous vehicles, medical image analysis, surveillance, augmented reality, and more.

AI With Python Tutorial

In this AI with Python tutorial, you’ll explore the fundamental and advanced concepts of artificial intelligence (AI) using the Python programming language. Whether you’re a complete beginner or an experienced professional, this tutorial is tailored to meet your learning needs, offering a step-by-step approach to mastering AI techniques with Python.

From understanding basic concepts to exploring advanced algorithms and applications, this tutorial equips you with the essential skills and knowledge to dive into the exciting world of AI. Whether you’re looking your build your career in AI or enhance your existing skills, this tutorial provides a solid foundation for your AI journey.