Ned  McGlynn

Ned McGlynn

1621453560

Transfer Learning (Computer Vision) For Beginners

Transfer Learning (Computer Vision) For Beginners

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Transfer Learning (Computer Vision) For Beginners
Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Why you should learn Computer Vision and how you can get started

I. Motivation

In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?

II. What is Computer Vision?

Image for post

Figure 1: Portrait of Larry Roberts.
The field of Computer Vision dates back to the 1960s when Larry Roberts, who is now widely considered as the “Father of Computer Vision”, published his paper _Machine Perception of Three-Dimensional Solids _detailing how a computer can infer 3D shapes from a 2D image (Roberts, 1995). Since then, other researchers have made amazing contributions to the field. These advances, however, have not changed the underlaying goal of Computer Vision which is to mimic the human visual system. From an engineering point of view, this means being able to build autonomous systems that can do things a human visual system can do such as detecting and recognizing objects, recognizing faces and facial expressions, etc. (Huang, 1996). Traditionally, many approaches in Computer Vision involves manual feature extraction. This means manually finding some unique features/characteristics (edges, shapes, etc) that are only present in an object to be able to detect and recognize what that object is. Unfortunately, one major issue arises when trying to detect and recognize variations (sizes, lightning conditions, etc) of that same object. It is difficult to find features that can uniquely identify an object across all variations. Fortunately, this problem is now solved with the introduction of Machine Learning, particularly a sub-field of Machine Learning called Deep Learning. Deep Learning utilizes a form of Neural Networks called Convolutional Neural Networks (CNNs). Unlike the traditional methods, methods that utilize CNNs are able to extract features automatically. Instead of trying to figure out which features can represent an object manually, a CNN can learn those features automatically by looking at many variations of that same object. As result, many recent advancements in the field of Computer Vision involves the use of CNNs.

#computer-science #machine-learning #deep-learning #computer-vision #learning #deep learning

4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning

_Before we start, if you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let’s connect via Linkedin! Please do not hesitate to send a contact request! _Orhan G. Yalçın — Linkedin

If you have been trying to build machine learning models with high accuracy; but never tried Transfer Learning, this article will change your life. At least, it did mine!

Most of us have already tried several machine learning tutorials to grasp the basics of neural networks. These tutorials were very helpful to understand the basics of artificial neural networks such as Recurrent Neural NetworksConvolutional Neural NetworksGANs, and Autoencoders. But, their main functionality was to prepare you for real-world implementations.

Now, if you are planning to build an AI system that utilizes deep learning, you either (i) have to have a very large budget for training and excellent AI researchers at your disposal or (ii) have to benefit from transfer learning.

#computer-vision #deep-learning #artificial-intelligence #transfer-learning #machine-learning

Alfredo  Sipes

Alfredo Sipes

1617715380

Why You Should Learn Computer Vision and How You Can Get Started

In today’s world, Computer Vision technologies are everywhere. They are embedded within many of the tools and applications that we use on a daily basis. However, we often pay little attention to those underlaying Computer Vision technologies because they tend to run in the background. As a result, only a small fraction of those outside the tech industries know about the importance of those technologies. Therefore, the goal of this article is to provide an overview of Computer Vision to those with little to no knowledge about the field. I attempt to achieve this goal by answering three questions: What is Computer Vision?, Why should you learn Computer Vision? and How you can get started?

#computer-science #machine-learning #deep-learning #computer-vision #learning

Self-Supervised Learning Methods for Computer Vision

Self-supervised Learning is an unsupervised learning method where the supervised learning task is created out of the unlabelled input data.
This task could be as simple as given the upper-half of the image, predict the lower-half of the same image, or given the grayscale version of the colored image, predict the RGB channels of the same image, etc.

#self-supervised-learning #representation-learning #deep-learning #computer-vision #unsupervised-learning