Deep Learning(CS7015): Lec 1.4 From Cats to Convolutional Neural Networks
Summary
TLDRThis script delves into the history of Convolutional Neural Networks (CNNs), humorously dubbed 'cats' due to its origins from a 1959 experiment observing cats' brain responses to visual stimuli. The experiment by Hubel and Wiesel laid the groundwork for the Neocognitron in 1980, a precursor to modern CNNs. The concept was further developed by Yan LeCun in 1989 for handwritten digit recognition, particularly for postal services. The script highlights the evolution of CNNs, their initial use in digit recognition, and their significance in the MNIST dataset, which is still pivotal in neural network education and experimentation.
Takeaways
- 🧠 The script discusses the history of Convolutional Neural Networks (CNNs), starting with the famous experiment by Hubel and Wiesel in 1959, which involved observing a cat's brain responses to different visual stimuli.
- 🔍 The experiment revealed that different neurons in the brain respond to different types of visual stimuli, laying the groundwork for the concept of feature detection in CNNs.
- 📚 The Neocognitron, proposed in 1980, is considered a precursor to modern CNNs, representing an early attempt at mimicking the brain's visual processing system.
- 👨🏫 Yan LeCun is credited with proposing the modern form of CNNs in 1989, with the initial application aimed at recognizing handwritten digits for postal services automation.
- 📬 The need for automatic recognition of postal codes and phone numbers on postcards led to the development and application of CNNs in practical scenarios.
- 📈 Over the years, there have been several improvements to CNNs, enhancing their capabilities and applications in various fields.
- 📊 The MNIST dataset, released in 1998, has become a cornerstone for teaching and experimenting with deep neural networks, including CNNs.
- 🔧 The script humorously notes that an algorithm inspired by an experiment on cats is now used to detect cats in videos, highlighting the evolution and versatility of CNNs.
- 🌐 The script implies the wide-ranging applications of CNNs beyond just cat detection, indicating their importance in various domains such as image and video analysis.
- 🎓 The historical context provided in the script is crucial for understanding the development and significance of CNNs in the field of artificial intelligence.
- 🔬 The script underscores the interdisciplinary nature of CNNs, drawing inspiration from neuroscience, computer science, and practical applications.
Q & A
What significant experiment was conducted by Hubel and Wiesel in 1959 involving a cat?
-Hubel and Wiesel conducted an experiment where they displayed lines of different orientations to a cat and measured which parts of the brain responded to these visual stimuli using electrodes. This experiment helped to understand that different neurons in the brain fire in response to different types of stimuli.
What was the outcome of the Hubel and Wiesel experiment that influenced the development of Convolutional Neural Networks (CNNs)?
-The experiment revealed that different neurons in the brain respond to different types of visual stimuli, which is the fundamental concept behind the development of Convolutional Neural Networks.
When was the Neocognitron proposed and what was its significance in the history of CNNs?
-The Neocognitron was proposed in 1980 and can be considered a primitive version of what we now know as Convolutional Neural Networks.
Who is credited with proposing modern Convolutional Neural Networks and for what purpose?
-Yann LeCun is credited with proposing modern Convolutional Neural Networks in 1989, initially for the task of handwritten digit recognition.
In what context were Convolutional Neural Networks first used according to the script?
-Convolutional Neural Networks were first used in the context of postal delivery services to automatically read and categorize handwritten pin codes and phone numbers on postcards.
When was the MNIST dataset released and what is its relevance today?
-The MNIST dataset was released in 1998 and remains relevant today as it is a popular dataset used for teaching deep neural networks and for initial experiments with various neural network-based models.
What is the MNIST dataset commonly used for?
-The MNIST dataset is commonly used for training and testing deep neural networks, particularly in the field of image recognition for handwritten digits.
How has the script humorously connected the origin of CNNs to their current applications?
-The script humorously notes that an algorithm inspired by an experiment on cats is now used to detect cats in videos, highlighting the evolution and application of CNNs.
What improvements have been made to Convolutional Neural Networks over the years as mentioned in the script?
-The script does not detail specific improvements but mentions that several enhancements have been made to Convolutional Neural Networks since their initial proposal in 1989.
Why were electrodes used in the Hubel and Wiesel experiment?
-Electrodes were used to measure the brain's response to different visual stimuli, helping to identify which parts of the brain's neurons fire in response to specific types of visual input.
What is the significance of the year 1989 in the context of Convolutional Neural Networks mentioned in the script?
-The year 1989 is significant because it marks when Convolutional Neural Networks were first proposed for use in handwritten digit recognition, specifically for postal services.
Outlines
😺 The Birth of Convolutional Neural Networks
This paragraph delves into the origins of Convolutional Neural Networks (CNNs), drawing a parallel to the famous experiments conducted by Hubel and Wiesel in 1959. They observed how different neurons in a cat's brain responded to various visual stimuli, such as lines of different orientations. This discovery laid the groundwork for the concept of feature extraction in CNNs. The narrative then transitions to the Neocognitron, proposed in 1980, which can be considered an early form of CNN. The paragraph also mentions Yan LeCun's significant contribution in 1989 with the introduction of modern CNNs for handwritten digit recognition, particularly for postal services to automate the sorting of mail based on zip codes. The MNIST dataset, released in 1998, is highlighted as a pivotal resource for teaching and experimenting with deep neural networks, and its enduring relevance is noted. The paragraph concludes with a humorous note on how an algorithm inspired by a cat experiment is now used to detect cats in videos.
Mindmap
Keywords
💡Convolutional Neural Networks (CNNs)
💡Hubel and Wiesel
💡Neocognitron
💡Yan Li Kun
💡Handwritten Digit Recognition
💡MNIST Dataset
💡Feature Detection
💡Filters
💡Deep Neural Networks
💡Visual Cortex
💡Postal Codes
Highlights
Hubel and Wiesel's 1959 experiment with a cat, involving visual stimuli and brain responses.
Different neurons in the brain respond to different types of visual stimuli.
Introduction of the Neocognitron in 1980, a primitive form of convolutional neural networks.
Yan Li Kun's proposal of modern convolutional neural networks in 1989 for handwritten digit recognition.
The context of postal delivery services for the development of convolutional neural networks.
The MNIST dataset's release in 1998 for teaching and experimenting with neural networks.
The MNIST dataset's continued use in neural network courses and assignments.
The historical significance of the cat experiment in shaping the development of convolutional neural networks.
The evolution of convolutional neural networks from the Neocognitron to modern applications.
The practical application of convolutional neural networks in postal code recognition for sorting mail.
The role of convolutional neural networks in improving automatic reading of handwritten information on postcards.
The humorous connection between the cat experiment and the current use of CNNs to detect cats in videos.
The importance of the MNIST dataset in the field of neural networks for both educational and experimental purposes.
The ongoing relevance of the MNIST dataset in various assignments and experiments in neural network courses.
The foundational work of Hubel and Wiesel that laid the groundwork for understanding neural responses to visual stimuli.
The innovative approach of using brain response data to inspire the creation of convolutional neural networks.
The practical implications of convolutional neural networks in sorting mail based on postal codes and categories.
Transcripts
I will talk about the history of Convolutional Neural Networks, and I call this part of history
as cats and it will become obvious why I call it so.
So, around 1959 Hubel and Wiesel did this famous experiment they are still I think you
could see some videos of it on YouTube, where there is this cat and there was a screen in
front of it and on the screen there were these lines being displayed at different locations
and in different orientations right.
So, slanted, horizontal, vertical and so on and there are some electrodes fitted to the
cat and they were measuring trying to measure that which parts of brain actually respond
to different visual stimuli.
Let us say if you show it stimulus at a certain location, does the different part of the brain
fire and so on right.
So, and one of the things of outcomes of the study was that, that different neurons in
brain fire to only different types of stimuli, it is not that all neurons in brain always
fire to any kind of visual stimuli that you give to them right.
So, this is essentially roughly the idea behind convolutional neural networks starting from
something known as Neocognitron, which was proposed way back in 1980.
You could think of it as a very primitive convolutional neural network, I am sure that
most of you have now read about or heard about convolutional neural networks, but something
very similar to it was proposed way back in 1980.
And what we know as the modern convolutional neural networks maybe I think Yan Li Kun is
someone who proposed them way back in 1989, and he was interested in using them for the
task of handwritten digit recognition and this was again in the context of postal delivery
services right.
So, lot of pin codes get written or phone numbers get written on the postcards and there
was a requirement to read them automatically.
So, that they can be the letters or postcards can be separated into different categories
according to the postcard according to the postal code and so on right so or the pin
code.
So, that is where this interest was there and 1989 was when this convolutional neural
networks were first proposed or used for this task.
And then over the years, several improvements were done to that; and in 1998 this now how
famous data set the MNIST data set which is used for teaching deep neural networks, courses
or even for initial experiments with various neural network based networks.
This is one of the popular data sets, which is used in this field and this was again released
way back in 1998 and even today even for my course I use it for various assignments and
so on.
So, it is interesting that an algorithm which was inspired by an experiment on cats is,
today used to detect cats in videos of course, among other various other things is just I
am just jokingly saying this.
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