Computer Vision course co-created with Affectiva and Nvidia
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Simultaneous Localization and Mapping (SLAM), Object Tracking, Image Classification, Deep Learning, Computer Vision, Object Tracking, Object Localization
ABOUT COMPUTER VISION NANODEGREE
Computer Vision is considered to be the future of new technologies. This is why you definitely need a broad portfolio of applications you have built. Completing the Computer Vision course is a great option for those who want to learn how to write a code that performs both facial recognition and scene understanding to object tracking. The course covers all the latest techniques in Computer Vision.
Among the others, you will learn multi-object recognition models, as well as deep learning architectures (R-CNN and YOLO – you will learn how to combine the networks to create an automatic image captioning app). The Computer Vision Experts will show you how to implement object tracking methods (like Simultaneous Localization and Mapping). The techniques you’ll learn are used e.g. in self-driving car navigation and drone flight.
This Computer Vision course takes around 3 months to complete. To enroll, you need some experience with Python, statistics, machine learning, and deep learning.
What is Computer Vision?
As a branch of artificial intelligence (AI), computer vision is the ability of computers and systems to analyze digital pictures, videos, and other visual inputs in order to extract meaningful information and take action or make suggestions. Computer vision and artificial intelligence work hand in hand to provide computers the ability to view, observe, and comprehend the world around them.
Computer vision is a lot like human eyesight, except that humans have an advantage since they’ve been around longer. Human vision has the benefit of a lifetime of context to learn how to identify separate things, how far away they are, whether they will be moving, or whether a picture is wrong.
In contrast to the human visual cortex, computer vision uses cameras, data, and algorithms to teach robots to do these activities in a fraction of the time. Systems that are taught to check items or monitor production assets may soon outperform humans since they can assess hundreds of products or processes in a minute and detect minute flaws or problems.
The market for computer vision is expanding across a wide range of sectors, from energy and utilities to production and automotive.
How does it work?
Computer vision requires a large amount of data. Images are recognized through a series of recurrent analyses of data. For example, in order to teach a computer to identify automotive tires, a large number of tire photos and tire-related items must be sent to the computer in order for it to understand the distinctions and recognize a tire, particularly one without any faults.
To do this, two important technologies are used: deep learning and convolutional neural networks (CNN).
It is possible for computers to learn about the context of visual input using algorithmic models, which is what machine learning relies on. It is possible that the computer can learn to distinguish between images if enough data is supplied to the model. Algorithms allow the computer to learn rather than being programmed to identify a picture.
In order to “see” a picture, a CNN breaks it down into pixels that are labeled with tags. Uses labels to do convolutions (mathematical operations on two functions to generate the third function) and makes predictions about what it sees. This process is repeated until the neural network is able to accurately anticipate what will happen in a sequence of convolutions. A human-like capacity for picture recognition and perception is achieved.
Benefits of Computer Vision
Several tasks may be automated using computer vision, which eliminates the need for human involvement. As a consequence, it offers several advantages to organizations:
- A more efficient and straightforward workflow is made possible by computer vision systems, which can do repetitive and tedious jobs at a quicker pace than people can.
- Improved goods and services No errors will be made by a computer vision system that has been taught thoroughly. Faster and better goods and services will be delivered as a consequence.
- Computer vision eliminates the need for companies to spend money correcting their problematic processes, allowing them to save money.
Employer demand for AI (Artificial Intelligence) – related roles has more than doubled over the past three years.
The average salary of a Computer Vision Engineer in the United States is $104k
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