My Little Pony Resume
My Little Pony Resume – One of the reasons why artificial intelligence and neural nets became so popular is probably the open course of professor Fei Fei Li’s CS231n visual recognition at Stanford. Along with many other influences like Andrew Ngai, they attracted a lot of attention to deep learning. Whether it’s detecting a cancerous tissue in medical images, adding cute animal ears on a selfie, or driving a car on the road, Computer Vision (CV) paints us with a bright future where machines can make our lives easier in many ways. .
In this article, let’s first learn about some of the history related to CV. Then we go to some special methods in CV such as lines and points. After we’ve built the basics and definitions, we’ll get to the details of how modern CV templates work. After a brief description of deep learning, we will go into more detail on the basics of convolutional neural networks. In the end, we will look at some modern algorithms today.
Table Of Contents
- 1 My Little Pony Resume
- 2 Resume Icons, My Little Pony Birthday, My Little Pony, Resume, My Chemical Romance, Hello My Name Is #946303
- 3 Lesson 6: Resumes And The Moment Before
My Little Pony Resume
Computer Vision, a Brief History “If we want machines to think, we must teach them to see.” — Fei Fei Li
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It was transferred from artificial intelligence, research in the field of CV started in the 1960s. One of the earliest relevant papers was probably Receptive Fields of Single Neurons in the Cat’s Striate Cortex by Hubel and Wiesel in 1959, where they introduced electrical current into the cat’s brain and observe the response after changing what the cat is seeing.
2 published another paper in 1962, giving their words to a more detailed study of how the cat’s brain uses visual information. The first studies in a way related to neuroscience were quite negative. I mentioned in one of my previous articles, that the policy-network-network went through a dark period until the 2000s.
There were other high places in the field. For example, the change of Hough, named after a patent in 1962 after the change of the patent of Paul Hough of Hough is now used in organizations such as autonomous driving. The edge analysis introduced by John Canny in 1986 is widely used in edge analysis. Edge detection is used in many different fields such as face recognition.
When I was studying at the University of Queensland in Canada (where Elon Musk also went), Professor Greenspan also showed their progress in permanent materials for machines. Canadians have contributed greatly to the field of AI research, including the famous CIFAR.
Resume Icons, My Little Pony Birthday, My Little Pony, Resume, My Chemical Romance, Hello My Name Is #946303
Another famous person to mention before we get into deep learning is Lenna Forsén. If you are in any field related to the use of digital images, you have probably seen his picture somewhere. Lawrence Roberts was the first person to use his photo from Playboy magazine in 1960 in his master’s thesis.
The film then became a standard model in the field of film production. Playboy magazine planned to file a lawsuit for the rights to the photos but graciously gave in when they realized they were for research and education.
Let’s go back to neural networks. Inspired by the work of Hubel and Wiesel, Kunihiko Fukushima developed the “Neocognitron” in 1980. The Neocognitron is arguably the first description of a convolutional neural network. The network was used to recognize handwritten characters.
Yann LeCun, known for his work on convolutional neural networks, used back propagation in convolutional neural networks in 1989. Then he published LeNet-5 in 1998 with successive learning algorithms.
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The revolution of neural networks happened in 2012. Alex Krizhevsky, with his AlexNet, won the ImageNet competition on September 30, showing the high performance of methods based on networks convolutional neural.
The famous scientist and the main researcher of ImageNet, professor Fei Fei Li, started working on the concept of ImageNet since 2006. He continued to travel to conferences and lectures all over the world, inspiring others in the field of see computer.
Another notable mention may be Joseph Redmon. Redmon created the YOLO network in 2016. The word YOLO seems to have come from a popular internet slang “You’re the Only One”, suggesting that people live their lifetime and is often used when young people are about to engage in a risky activity. In the document, YOLO stands for “You only see it once”, which offers instant visual effects based on neural networks.
Redmon also considers himself a pony in his resume, referring to “My Little Pony”, an American brand aimed at little girls.
Bloom & Gloom
Advances in computer vision have improved our world in many different ways. It’s time we get to how those algorithms work!
Since the time of television, your monitor displays an image by adjusting the light of Cathode Ray Tubes (CRT) in 3 different colors — Red (R), Green (G) and Blue (B). So even though our screens today probably use more advanced devices like Liquid Crystal Display (LCD) or Organic Light-Emitting Diodes (OLED), images are still simply store and transfer numbers through an RGB table format. Bitmaps, for example, store images in a sequence of hexadecimal characters starting from 0x000000 to 0xFFFFFF, which can represent the 3 digits from 0 to 255.
Some image formats may store the image differently due to compression or other manipulations, but are usually returned in RGB values. In terms of mathematical usage, the difference in RGB does not represent their true difference in terms of human understanding. The Commission InternationaledeL’éclairage (CIE) received Δ
The way to store computers and process information is another big field, which we can find more at another time in another article.
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Experimental Research and Visual Information “A visual image is created by our brain’s ability to put together parts of pixels in the shape of the edges.” — Jill Bolte Taylor
Analyzing an image accurately is difficult, as the array of pixels can be complex and noisy. This is why researchers often look for shapes like edges and lines. These features can be represented by simpler numbers or relationships. There are many ways to determine the edge such as using a grid or Canny’s method. In this article, we will briefly summarize Sobel’s method.
Edges have very high contrast in pixels, and most eye detection methods try to remove areas where the difference between pixels is visible. Sobel’s method detects edges by applying constraints (denoted by operator *) to 3×3 regions of the image with 2 special kernels. kernel will produce horizontal and vertical components of the gas which can be used to calculate a vector representing the direction and strength of the edges.
More information on the number can be found on Wikipedia, and is explained in the video below. The author also made videos on other image processing algorithms such as Canny edge detector and blur filters.
Lesson 6: Resumes And The Moment Before
Imagine that we are engineers working for a car company that wants to develop its self-driving cars. At some point, we have to learn the car to drive in the road on the road. Otherwise, our car will drive like the Asian female driver in Family Guy.
If the car lines are continuous, it’s easy. We can be sure that the car will go back a little if it is too close to either side of the line. But we know that in many places, California for example, there are broken lines on the road. If the car only knows how close it is to the lines, it will probably veer into those gaps between the hard lines.
Thanks to the Hough transform, we will be able to recover the straight line from the parallel lines. The Hough transform converts a line in xy space to a point in m-c space. A point in xy space, however, will turn into a line in m-c space. The m-c space also has the property that all lines connected to the same point will correspond to the points on the same line in the x-y space. For more information about line detection by Hough Transform, in a document.
Not only does Hough Transforms recognize lines, it also recognizes circles. Read about Circle Hough Transform and Generalized Hough Transform to find out more!
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Advances in Deep Learning: Convolutional Neural Networks & Residual Neural Networks “No one tells a child how to see, especially in the early years. They learn this through real experiences and examples. — Fei Fei Li
The term “Machine Learning” was first coined by Arthur Samuel in 1952. However, research on neural networks was very slow until the 2000s. The first model of a computer neuron was developed by neurophysiologist Warren McCulloch and mathematician Walter Pitts in 1943. It was intended to explain how neurons work in inside our brains.
Convolutional Neural Networks (CNN) became a star after the ImageNet 2012 competition. Since then, we started to see neural network models in over 80% of new papers published in any conceivable field. there. There were many other machine learning algorithms such as k-NN and Random Forest but they outperformed CNN in terms of image processing.
Whenever convolutional neural networks are mentioned, the first person who comes to mind as a data scientist is probably Yann LeCun, who published his paper LeNet-5 in 1998.
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Generally, CNN is made up of 4 types of tables. I mention 3 in my Alpha Go article because they are 3