Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com
Last Updated: Mar 27, 2024
Difficulty: Medium

Deep Learning vs Computer Vision

Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

Deep Learning and Computer Vision are currently very hot issues since they improve the convenience of our life. Everything from predicting our wants to finding the ideal things on marketplaces to autonomous vehicles is already possible today. Such technologies appeared to us early on to be the call of the future, but in fact, we have advanced to their regular use in numerous industries.

Deep Leaning vs Computer Vision


Every area of the technology industry, including start-ups, is rushing to catch up with the competition by focusing on computer vision and deep learning, two of the industry's hottest topics right now. In this blog, you will get to know what Deep Learning and Computer Vision are, their importance, and various points of view on Deep Learning vs Computer Vision.

What is Artificial Intelligence (AI)?

Artificial intelligence (AI) is a broad field of computer science concerned with creating intelligent machines capable of doing activities that normally require human intelligence. While AI is an interdisciplinary discipline with many techniques, advances in machine learning and deep learning, in particular, are causing a paradigm shift in almost every sector of the IT industry. 

Artificial intelligence enables machines to mimic, if not outperform, the capabilities of the human mind. From self-driving cars to the emergence of generative AI tools like ChatGPT and Google's Bard, AI is fast becoming a part of everyday life and a field in which organizations of all sizes are investing.

Get the tech career you deserve, faster!
Connect with our expert counsellors to understand how to hack your way to success
User rating 4.7/5
1:1 doubt support
95% placement record
Akash Pal
Senior Software Engineer
326% Hike After Job Bootcamp
Himanshu Gusain
Programmer Analyst
32 LPA After Job Bootcamp
After Job
Bootcamp

Component of AI

components of AI
  • Machine Learning: It is always working to improve the way a machine learns. A machine, like a human, can draw inferences and improve after making a mistake or going through a cycle of activity, which does not require detailed programming
     
  • Deep Learning: It is a subtype of machine learning that employs neural networks. The human brain is the source of inspiration for its application. In this vein, the machine attempts to discover data connections
     
  • Cognitive Computing: It is a system that facilitates communication between humans and machines. The primary tasks involve analyzing voice, language, and visuals
     
  • Neural Network: Neural networks are man-made systems that resemble the biological neural networks found in the human body very closely. A set of algorithms called a neural network imitates how the human brain functions by attempting to identify hidden links in a set of data
     
  • Computer Vision: It is the scientific study of how computers use images to learn and understand concepts. They use algorithms, artificial intelligence, and self-learning processes to translate visual input into ideas and then decide the appropriate actions to take

What is Deep Learning?

Deep Leaning

Deep Learning is a subset of Machine Learning that uses Artificial Neural Networks (ANNs) to replicate the functioning of the human brain. Deep Learning refers to an Artificial Neural Network that is composed of an interconnected web of thousands or millions of neurons layered in numerous layers.

A neural network works like this: you input the neural network vast amounts of data, which are subsequently processed by the neurons. Each neuron has a function for activity. When a certain threshold is met, the neurons become active, and their values are distributed across the neural network.

Deep Learning is concerned with huge neural networks being trained on massive amounts of data. Deep Learning presents an amazing opportunity because everyday global data collection is off the charts right now (and will only increase in the future). This is because the more data fed into a huge neural network, the better it performs. Deep Learning is frequently used in areas such as Predictive Analytics, Computer Vision, and Object Recognition.

What is Computer Vision?

The goal of computer vision is to give computer systems human-like visual perception abilities. It is an interdisciplinary field that enables computer systems to process, evaluate, and interpret our visual world accurately. Computer vision, for example, trains computers to recognize significant information in photos and video files in the same manner that humans do. 

Machine learning, a component of artificial intelligence that focuses on 'training' machines to learn on their own over time, is used in modern computer vision. An acceptable response will be determined by a machine learning system's analysis of prior experiences and decisions, as opposed to a system that always follows a set of predetermined rules or instructions. In addition, all of this may be done with little or no human involvement.

Uses of Deep Learning in Computer Vision

Deep learning technologies have enabled the construction of more accurate and complicated computer vision models. The incorporation of computer vision applications is becoming increasingly beneficial as these technologies advance. The following are a few examples of how deep learning is being used to improve computer vision.

  • Image Localization: It is the process of determining where items are located in an image. Objects are indicated with a bounding box once they have been identified. Object detection takes this a step further by classifying the objects that are identified. CNNs such as AlexNet, Fast RCNN, and Faster RCNN are used in this process
     
  • Semantic segmentation: It is similar to object detection in that it relies on certain pixels associated with an object. This allows image objects to be more precisely specified and eliminates the need for boundary boxes. Fully convolutional networks (FCN) or U-Nets are frequently used for semantic segmentation
     
  • Pose estimation: It is a technique for determining where joints are in a photograph of a person or object and what that placement signifies. It works with both 2D and 3D photos. PoseNet, a CNN-based network, is the major architecture used for posture estimation

Deep Learning vs Computer Vision

Category Deep Learning Computer Vision
concept It is an area of artificial intelligence that uses artificial neural networks to replicate how the human brain works. It is an area of machine learning that allows computers to process, examine, and comprehend visual data.
Purpose The goal of this endeavor is to enable machines to achieve some level of comprehension and knowledge similar to how the human brain processes information. The goal of this endeavor is to program a computer to grasp the visual information included within image and video data in order to derive helpful insights.
Applications Self-driving cars, natural language processing, visual recognition, picture and audio recognition, virtual assistants, and other comparable technologies are examples of applications. Among its numerous uses are flaw identification, image labeling, face recognition, and other related tasks.

Also see, Artificial Intelligence in Education

Frequently Asked Questions

What is the meaning of overfitting?

When working with Deep Learning, overfitting is a prevalent problem. It is a scenario in which the Deep Learning algorithm fiercely searches the data for valid information. As a result, the Deep Learning model detects noise rather than usable data, resulting in very high variance and low bias.

What are activation functions?

In Deep Learning, activation functions are entities that are utilized to transform inputs into useful output parameters. It is a function that determines whether or not a neuron requires activation by computing the weighted sum with the bias.

What's the purpose of grayscaling?

The range of a digital image's whiteness to blackness is known as grayscale. The technique by which programmers change a colored image to grayscale is known as grayscaling. The visual information is made simpler as a result, making it easier for a computer to process.

What is a digital image?

A digital image is a picture made up of tiny elements known as pixels. These pixels are composed of numerical components that represent the color codes and intensity of the pixels. These numbers are used by AI systems to comprehend an image.

Conclusion

Computer vision is a branch of artificial intelligence that tries to give computers the ability to perceive and interpret digital data, including inside images and videos. Deep learning is a subfield of machine learning that aims to bring us one step closer to artificial intelligence, which was one of machine learning's initial goals. The blog clearly describes the importance of both and provides various points on Deep Learning vs Computer Vision.

To better understand the topic, you can refer to What is Machine LearningRegister in Computer, and Repeater in Computer Network.

You can also consider our Machine Learning Course to give your career an edge over others.

Topics covered
1.
Introduction
2.
What is Artificial Intelligence (AI)?
3.
Component of AI
4.
What is Deep Learning?
5.
What is Computer Vision?
6.
Uses of Deep Learning in Computer Vision
7.
Deep Learning vs Computer Vision
8.
Frequently Asked Questions
8.1.
What is the meaning of overfitting?
8.2.
What are activation functions?
8.3.
What's the purpose of grayscaling?
8.4.
What is a digital image?
9.
Conclusion