Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:
- Discriminative tasks
- Generative tasks
Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.
Discriminative Computer Vision finds applications in image classification, object detection, object recognition, shape detection, pose estimation, image segmentation, etc. Generative Computer Vision finds applications in photo enhancement, image synthesis, augmentation, deepfake videos, etc.
This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!
Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.
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