Mike  Alreend

Mike Alreend


How can we learn about AR?

Fascinating experiences have been a very frequent issue owing to technical advancements. It is easy for us to fly everywhere in the world using our virtual reality computer. That is virtual reality or VR. Operating Systems have grown into a broad variety of devices. Companies are utilising the changed planet to market their goods.

Augmented reality is an innovative phenomenon and can be really thrilling. However, the growth in the GDP just in the last few years. In 2024, the augmented reality industry is projected to cross an approximate $72.7 billion.Augmented reality is an interesting industry to be interested , and this is fantastic news for investors and innovators.



Teaching and motivating capabilities can be integrated by Virtual Reality. Guidance is the way to cope with this situation. What keeps you from dreaming about the virtual world?

It is easy today to receive knowledge about the new findings. Studying has become more important thanks to the usage of the internet. This forum offers a place for knowledge exchange. If you operate in this field, you will play a key role in VR.

Of course this is not acceptable. You should read more about Internet from authentic sources. In today’s environment, you ought to get acquainted with virtual reality.

Global Engineering Council is the right venue to think about virtual reality. This article would advise you on augmented reality and its implementations.

The meaning of your AR definition remains as it is implemented in the actual world. It is easier to teach publicly, just as in a school.

Technology exists inside each and every individual. You will engage in a social network forum to encounter new people and read more about them. The more you indulge in something, the better you get at it.

Virtual reality would have a major influence on the planet because of its variety.

#virtualrealitycertification #vr certification #virtualrealityexpert #virtualrealitycourses

What is GEEK

Buddha Community

How can we learn about AR?
Jerad  Bailey

Jerad Bailey


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

Anastasia soda

Anastasia soda


10 Highest Paying Jobs You Can Learn (Without College). ( HOT NEWS!!! )

These are the 10 highest paying jobs you can learn without needing a college degree. Jobs that pay $75,000 and higher.
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🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #10 highest paying jobs you can learn #jobs #highest paying jobs you can learn #10 highest paying jobs you can learn (without college)

sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

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

Jackson  Crist

Jackson Crist


Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

  • Temporal difference learning (TD learning)
  • Parameters
  • QL & SARSA
  • Comparison
  • Implementation
  • Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning