-
But what is a neural network? | Deep learning chapter 1
What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
Additional funding for this project was provided by Amplify Partners
Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy
There are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, ...
published: 05 Oct 2017
-
Neural Networks Explained in 5 minutes
Learn more about watsonx: https://ibm.biz/BdvxRs
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Master Inventor, Martin Keen, makes some important points about neural networks and does it all in 5 minutes.
#Software #ITModernization #NeuralNetworks #DataFabric #lightboard #IBM
published: 24 May 2022
-
Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn
🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Post Graduate Program in AI and Machine Learning - https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitg-generative-ai-mach...
published: 19 Jun 2019
-
Explained In A Minute: Neural Networks
Artificial Neural Networks explained in a minute.
As you might have already guessed, there are a lot of things that didn't fit into this one-minute explanation. You can read my accompanying blogpost for some more details on things I might have left out: https://arztsamuel.github.io/en/blogs/2018/EiaM-NeuralNetworks.html
If you like these kind of videos and would like to see more technical topics explained in a minute, let me know by pressing the like button.
Don't miss any future videos, by subscribing to my channel.
Follow me on Twitter: https://twitter.com/SamuelArzt
Interested in this series? You can find more information about it on my website: https://arztsamuel.github.io/en/projects/youtube/explained/explained.html
This video was recorded with a potato.
Background Music: Drops ...
published: 02 Sep 2017
-
Neural Networks explained in 60 seconds!
Ever wondered how the famous neural networks work? Let's quickly dive into the basics of Neural Networks, in less than 60 seconds!
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: https://www.assemblyai.com
🐦 Twitter: https://twitter.com/AssemblyAI
🦾 Discord: https://discord.gg/Cd8MyVJAXd
▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning #neuralnetworks
published: 22 Jul 2022
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The Essential Main Ideas of Neural Networks
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.
English
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
Spanish
Este video ha sido doblado al español con voz artificial con https://aloud.area120.google.com para aumentar la accesibilidad. Puede cambiar el idioma de la pista de audio en e...
published: 31 Aug 2020
-
What is a Neural Network?
Texas-born and bred engineer who developed a passion for computer science and creating content 🌶️ .
Socials: https://zaradarz.com
published: 16 Aug 2024
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What Do Neural Networks Really Learn? Exploring the Brain of an AI Model
Neural networks have become increasingly impressive in recent years, but there's a big catch: we don't really know what they are doing. We give them data and ways to get feedback, and somehow, they learn all kinds of tasks. It would be really useful, especially for safety purposes, to understand what they have learned and how they work after they've been trained. The ultimate goal is not only to understand in broad strokes what they're doing but to precisely reverse engineer the algorithms encoded in their parameters. This is the ambitious goal of mechanistic interpretability. As an introduction to this field, we show how researchers have been able to partly reverse-engineer how InceptionV1, a convolutional neural network, recognizes images.
▀▀▀▀▀▀▀▀▀SOURCES & READINGS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
T...
published: 14 Jun 2024
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Human Brains vs Neural Networks #ai
published: 20 Jan 2025
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Neural Networks and Deep Learning: Crash Course AI #3
You can learn more about CuriosityStream at https://curiositystream.com/crashcourse.
Today, we're going to combine the artificial neuron we created last week into an artificial neural network. Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they've become one of the most dominant machine learning technologies used today.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following patrons for their generous monthly...
published: 23 Aug 2019
18:40
But what is a neural network? | Deep learning chapter 1
What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown
Written/interacti...
What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
Additional funding for this project was provided by Amplify Partners
Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy
There are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!
https://github.com/mnielsen/neural-networks-and-deep-learning
I also highly recommend Chris Olah's blog: http://colah.github.io/
For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs
For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville.
Also, the publication Distill is just utterly beautiful: https://distill.pub/
Lion photo by Kevin Pluck
Звуковая дорожка на русском языке: Влад Бурмистров.
Thanks to these viewers for their contributions to translations
German: @fpgro
Hebrew: Omer Tuchfeld
Hungarian: Máté Kaszap
Italian: @teobucci, Teo Bucci
-----------------
Timeline:
0:00 - Introduction example
1:07 - Series preview
2:42 - What are neurons?
3:35 - Introducing layers
5:31 - Why layers?
8:38 - Edge detection example
11:34 - Counting weights and biases
12:30 - How learning relates
13:26 - Notation and linear algebra
15:17 - Recap
16:27 - Some final words
17:03 - ReLU vs Sigmoid
Correction 14:45 - The final index on the bias vector should be "k"
------------------
Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim
If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind.
Music by Vincent Rubinetti.
Download the music on Bandcamp:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
Stream the music on Spotify:
https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people.
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that).
If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended
Various social media stuffs:
Website: https://www.3blue1brown.com
Twitter: https://twitter.com/3Blue1Brown
Patreon: https://patreon.com/3blue1brown
Facebook: https://www.facebook.com/3blue1brown
Reddit: https://www.reddit.com/r/3Blue1Brown
https://wn.com/But_What_Is_A_Neural_Network_|_Deep_Learning_Chapter_1
What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
Additional funding for this project was provided by Amplify Partners
Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that!
For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy
There are two neat things about this book. First, it's available for free, so consider joining me in making a donation to Nielsen if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!
https://github.com/mnielsen/neural-networks-and-deep-learning
I also highly recommend Chris Olah's blog: http://colah.github.io/
For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs
For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville.
Also, the publication Distill is just utterly beautiful: https://distill.pub/
Lion photo by Kevin Pluck
Звуковая дорожка на русском языке: Влад Бурмистров.
Thanks to these viewers for their contributions to translations
German: @fpgro
Hebrew: Omer Tuchfeld
Hungarian: Máté Kaszap
Italian: @teobucci, Teo Bucci
-----------------
Timeline:
0:00 - Introduction example
1:07 - Series preview
2:42 - What are neurons?
3:35 - Introducing layers
5:31 - Why layers?
8:38 - Edge detection example
11:34 - Counting weights and biases
12:30 - How learning relates
13:26 - Notation and linear algebra
15:17 - Recap
16:27 - Some final words
17:03 - ReLU vs Sigmoid
Correction 14:45 - The final index on the bias vector should be "k"
------------------
Animations largely made using manim, a scrappy open source python library. https://github.com/3b1b/manim
If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and has many other quirks you might expect in a library someone wrote with only their own use in mind.
Music by Vincent Rubinetti.
Download the music on Bandcamp:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
Stream the music on Spotify:
https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u
If you want to contribute translated subtitles or to help review those that have already been made by others and need approval, you can click the gear icon in the video and go to subtitles/cc, then "add subtitles/cc". I really appreciate those who do this, as it helps make the lessons accessible to more people.
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that).
If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended
Various social media stuffs:
Website: https://www.3blue1brown.com
Twitter: https://twitter.com/3Blue1Brown
Patreon: https://patreon.com/3blue1brown
Facebook: https://www.facebook.com/3blue1brown
Reddit: https://www.reddit.com/r/3Blue1Brown
- published: 05 Oct 2017
- views: 18297576
4:32
Neural Networks Explained in 5 minutes
Learn more about watsonx: https://ibm.biz/BdvxRs
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and ...
Learn more about watsonx: https://ibm.biz/BdvxRs
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Master Inventor, Martin Keen, makes some important points about neural networks and does it all in 5 minutes.
#Software #ITModernization #NeuralNetworks #DataFabric #lightboard #IBM
https://wn.com/Neural_Networks_Explained_In_5_Minutes
Learn more about watsonx: https://ibm.biz/BdvxRs
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Master Inventor, Martin Keen, makes some important points about neural networks and does it all in 5 minutes.
#Software #ITModernization #NeuralNetworks #DataFabric #lightboard #IBM
- published: 24 May 2022
- views: 311629
5:45
Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn
🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFol...
🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Post Graduate Program in AI and Machine Learning - https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Caltech - AI & Machine Learning Bootcamp (US Only) - https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
This video on What is a Neural Networkdelivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the different parameters used in a Neural Network such as weights, bias, and activation functions. We will also understand how to train a Neural Network using forward propagation and then adjust to the errors in the network using the backpropagation method. This video also covers a few popular Neural Network applications. Now, let us jump straight into learning what is a Neural Network.
0:00 What is a Neural Network?
0:33 How Neural Networks work?
03:43 Neural Network examples
04:21 Quiz
04:52 Neural Network applications
Don't forget to take the quiz at 04:21
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#NeuralNetwork #WhatIsANeuralNetwork #WhatAreNeuralNetworks #DeepLearningAndNeuralNetworks #DeepLearning #ArtificalNeuralNetwork #NeuralNetworkExplained #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn
➡️ About Caltech Post Graduate Program In AI And Machine Learning
Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.
✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- PGP AI & ML completion certificate from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle Membership
- Earn up to 22 CEUs from Caltech CTME
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- 8X higher interaction in live online classes by industry experts
✅ Skills Covered
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- Recommendation Systems
- NLP
- Neural Networks
- GANs
- Deep Learning
- Reinforcement Learning
- Speech Recognition
- Ensemble Learning
- Computer Vision
👉 Learn More At: 🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=Description&utm_source=Youtube
https://wn.com/Neural_Network_In_5_Minutes_|_What_Is_A_Neural_Network_|_How_Neural_Networks_Work_|_Simplilearn
🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITK - Professional Certificate Course in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Purdue - Post Graduate Program in AI and Machine Learning - https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥IITG - Professional Certificate Program in Generative AI and Machine Learning (India Only) - https://www.simplilearn.com/iitg-generative-ai-machine-learning-program?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
🔥Caltech - AI & Machine Learning Bootcamp (US Only) - https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=bfmFfD2RIcg&utm_medium=DescriptionFirstFold&utm_source=Youtube
This video on What is a Neural Networkdelivers an entertaining and exciting introduction to the concepts of Neural Network. We will learn the different layers present in a Neural Network and understand how these layers process data. We will get an idea of the different parameters used in a Neural Network such as weights, bias, and activation functions. We will also understand how to train a Neural Network using forward propagation and then adjust to the errors in the network using the backpropagation method. This video also covers a few popular Neural Network applications. Now, let us jump straight into learning what is a Neural Network.
0:00 What is a Neural Network?
0:33 How Neural Networks work?
03:43 Neural Network examples
04:21 Quiz
04:52 Neural Network applications
Don't forget to take the quiz at 04:21
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#NeuralNetwork #WhatIsANeuralNetwork #WhatAreNeuralNetworks #DeepLearningAndNeuralNetworks #DeepLearning #ArtificalNeuralNetwork #NeuralNetworkExplained #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn
➡️ About Caltech Post Graduate Program In AI And Machine Learning
Designed to boost your career as an AI and ML professional, this program showcases Caltech CTME's excellence and IBM's industry prowess. The artificial intelligence course covers key concepts like Statistics, Data Science with Python, Machine Learning, Deep Learning, NLP, and Reinforcement Learning through an interactive learning model with live sessions.
✅ Key Features
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- PGP AI & ML completion certificate from Caltech CTME
- Masterclasses delivered by distinguished Caltech faculty and IBM experts
- Caltech CTME Circle Membership
- Earn up to 22 CEUs from Caltech CTME
- Online convocation by Caltech CTME Program Director
- IBM certificates for IBM courses
- Access to hackathons and Ask Me Anything sessions from IBM
- 25+ hands-on projects from the likes of Twitter, Mercedes Benz, Uber, and many more
- Seamless access to integrated labs
- Capstone projects in 3 domains
- 8X higher interaction in live online classes by industry experts
✅ Skills Covered
- Statistics
- Python
- Supervised Learning
- Unsupervised Learning
- Recommendation Systems
- NLP
- Neural Networks
- GANs
- Deep Learning
- Reinforcement Learning
- Speech Recognition
- Ensemble Learning
- Computer Vision
👉 Learn More At: 🔥Artificial Intelligence Engineer (IBM) - https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=bfmFfD2RIcg&utm_medium=Description&utm_source=Youtube
- published: 19 Jun 2019
- views: 1515090
1:04
Explained In A Minute: Neural Networks
Artificial Neural Networks explained in a minute.
As you might have already guessed, there are a lot of things that didn't fit into this one-minute explanation...
Artificial Neural Networks explained in a minute.
As you might have already guessed, there are a lot of things that didn't fit into this one-minute explanation. You can read my accompanying blogpost for some more details on things I might have left out: https://arztsamuel.github.io/en/blogs/2018/EiaM-NeuralNetworks.html
If you like these kind of videos and would like to see more technical topics explained in a minute, let me know by pressing the like button.
Don't miss any future videos, by subscribing to my channel.
Follow me on Twitter: https://twitter.com/SamuelArzt
Interested in this series? You can find more information about it on my website: https://arztsamuel.github.io/en/projects/youtube/explained/explained.html
This video was recorded with a potato.
Background Music: Drops of H2O ( The Filtered Water Treatment ) by J.Lang (c) copyright 2012 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/djlang59/37792 Ft: Airtone
#NeuralNetworks #MachineLearning #Tutorial
https://wn.com/Explained_In_A_Minute_Neural_Networks
Artificial Neural Networks explained in a minute.
As you might have already guessed, there are a lot of things that didn't fit into this one-minute explanation. You can read my accompanying blogpost for some more details on things I might have left out: https://arztsamuel.github.io/en/blogs/2018/EiaM-NeuralNetworks.html
If you like these kind of videos and would like to see more technical topics explained in a minute, let me know by pressing the like button.
Don't miss any future videos, by subscribing to my channel.
Follow me on Twitter: https://twitter.com/SamuelArzt
Interested in this series? You can find more information about it on my website: https://arztsamuel.github.io/en/projects/youtube/explained/explained.html
This video was recorded with a potato.
Background Music: Drops of H2O ( The Filtered Water Treatment ) by J.Lang (c) copyright 2012 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/djlang59/37792 Ft: Airtone
#NeuralNetworks #MachineLearning #Tutorial
- published: 02 Sep 2017
- views: 498928
1:00
Neural Networks explained in 60 seconds!
Ever wondered how the famous neural networks work? Let's quickly dive into the basics of Neural Networks, in less than 60 seconds!
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬...
Ever wondered how the famous neural networks work? Let's quickly dive into the basics of Neural Networks, in less than 60 seconds!
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: https://www.assemblyai.com
🐦 Twitter: https://twitter.com/AssemblyAI
🦾 Discord: https://discord.gg/Cd8MyVJAXd
▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning #neuralnetworks
https://wn.com/Neural_Networks_Explained_In_60_Seconds
Ever wondered how the famous neural networks work? Let's quickly dive into the basics of Neural Networks, in less than 60 seconds!
▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬
🖥️ Website: https://www.assemblyai.com
🐦 Twitter: https://twitter.com/AssemblyAI
🦾 Discord: https://discord.gg/Cd8MyVJAXd
▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1
🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
#MachineLearning #DeepLearning #neuralnetworks
- published: 22 Jul 2022
- views: 418004
18:54
The Essential Main Ideas of Neural Networks
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are ...
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.
English
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
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Portuguese
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If you'd like to support StatQuest, please consider...
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0:00 Awesome song and introduction
2:01 A simple dataset and problem
3:37 Description of Neural Networks
7:54 Creating a squiggle from curved lines
15:25 Using the Neural Network to make a prediction
16:38 Some more Neural Network terminology
#StatQuest #NeuralNetworks #DubbedWithAloud
https://wn.com/The_Essential_Main_Ideas_Of_Neural_Networks
Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are "black boxes", but that's not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.
English
This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu.
Spanish
Este video ha sido doblado al español con voz artificial con https://aloud.area120.google.com para aumentar la accesibilidad. Puede cambiar el idioma de la pista de audio en el menú Configuración.
Portuguese
Este vídeo foi dublado para o português usando uma voz artificial via https://aloud.area120.google.com para melhorar sua acessibilidade. Você pode alterar o idioma do áudio no menu Configurações.
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
2:01 A simple dataset and problem
3:37 Description of Neural Networks
7:54 Creating a squiggle from curved lines
15:25 Using the Neural Network to make a prediction
16:38 Some more Neural Network terminology
#StatQuest #NeuralNetworks #DubbedWithAloud
- published: 31 Aug 2020
- views: 1025380
7:37
What is a Neural Network?
Texas-born and bred engineer who developed a passion for computer science and creating content 🌶️ .
Socials: https://zaradarz.com
Texas-born and bred engineer who developed a passion for computer science and creating content 🌶️ .
Socials: https://zaradarz.com
https://wn.com/What_Is_A_Neural_Network
Texas-born and bred engineer who developed a passion for computer science and creating content 🌶️ .
Socials: https://zaradarz.com
- published: 16 Aug 2024
- views: 1029759
17:35
What Do Neural Networks Really Learn? Exploring the Brain of an AI Model
Neural networks have become increasingly impressive in recent years, but there's a big catch: we don't really know what they are doing. We give them data and wa...
Neural networks have become increasingly impressive in recent years, but there's a big catch: we don't really know what they are doing. We give them data and ways to get feedback, and somehow, they learn all kinds of tasks. It would be really useful, especially for safety purposes, to understand what they have learned and how they work after they've been trained. The ultimate goal is not only to understand in broad strokes what they're doing but to precisely reverse engineer the algorithms encoded in their parameters. This is the ambitious goal of mechanistic interpretability. As an introduction to this field, we show how researchers have been able to partly reverse-engineer how InceptionV1, a convolutional neural network, recognizes images.
▀▀▀▀▀▀▀▀▀SOURCES & READINGS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
This topic is truly a rabbit hole. If you want to learn more about this important research and even contribute to it, check out this list of sources about mechanistic interpretability and interpretability in general we've compiled for you:
On Interpreting InceptionV1:
Feature visualization: https://distill.pub/2017/feature-visualization/
Zoom in: An Introduction to Circuits: https://distill.pub/2020/circuits/zoom-in/
The Distill journal contains several articles that try to make sense of how exactly InceptionV1 does what it does: https://distill.pub/2020/circuits/
OpenAI's Microscope tool lets us visualize the neurons and channels of a number of vision models in great detail: https://microscope.openai.com/models
Here's OpenAI's Microscope tool pointed on layer Mixed3b in InceptionV1: https://microscope.openai.com/models/inceptionv1/mixed3b_0?models.op.feature_vis.type=channel&models.op.technique=feature_vis
Activation atlases: https://distill.pub/2019/activation-atlas/
More recent work applying SAEs to InceptionV1: https://arxiv.org/abs/2406.03662v1
Transformer Circuits Thread, the spiritual successor of the circuits thread on InceptionV1. This time on transformers: https://transformer-circuits.pub/
In the video, we cite "Toy Models of Superposition": https://transformer-circuits.pub/2022/toy_model/index.html
We also cite "Towards Monosemanticity: Decomposing Language Models With Dictionary Learning": https://transformer-circuits.pub/2023/monosemantic-features/
More recent progress:
Mapping the Mind of a Large Language Model:
Press: https://www.anthropic.com/research/mapping-mind-language-model
Paper in the transformers circuits thread: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
Extracting Concepts from GPT-4:
Press: https://openai.com/index/extracting-concepts-from-gpt-4/
Paper: https://arxiv.org/abs/2406.04093
Browse features: https://openaipublic.blob.core.windows.net/sparse-autoencoder/sae-viewer/index.html
Language models can explain neurons in language models (cited in the video):
Press: https://openai.com/index/language-models-can-explain-neurons-in-language-models/
Paper: https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html
View neurons: https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html
Neel Nanda on how to get started with Mechanistic Interpretability:
Concrete Steps to Get Started in Transformer Mechanistic Interpretability: https://www.neelnanda.io/mechanistic-interpretability/getting-started
Mechanistic Interpretability Quickstart Guide: https://www.neelnanda.io/mechanistic-interpretability/quickstart
200 Concrete Open Problems in Mechanistic Interpretability: https://www.alignmentforum.org/posts/LbrPTJ4fmABEdEnLf/200-concrete-open-problems-in-mechanistic-interpretability
More work mentioned in the video:
Progress measures for grokking via mechanistic interpretability: https://arxiv.org/abs/2301.05217
Discovering Latent Knowledge in Language Models Without Supervision: https://arxiv.org/abs/2212.03827
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning: https://www.nature.com/articles/s41551-018-0195-0
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AAAA you don't fit in the description this time! But we thank you from the bottom of our hearts. All of you, in this Google Doc: https://docs.google.com/document/d/18S3cEkXrllXdWQMxL9G0KjB26YMZnbA4I4VHw5j55oA/edit?usp=sharing
▀▀▀▀▀▀▀CREDITS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
All the good doggos who worked on this video: https://docs.google.com/document/d/1KQZCfiv1nFKrAm9vcXNjNzQfTLVqY_ofXcWlgXH_dVY/edit?usp=sharing
https://wn.com/What_Do_Neural_Networks_Really_Learn_Exploring_The_Brain_Of_An_Ai_Model
Neural networks have become increasingly impressive in recent years, but there's a big catch: we don't really know what they are doing. We give them data and ways to get feedback, and somehow, they learn all kinds of tasks. It would be really useful, especially for safety purposes, to understand what they have learned and how they work after they've been trained. The ultimate goal is not only to understand in broad strokes what they're doing but to precisely reverse engineer the algorithms encoded in their parameters. This is the ambitious goal of mechanistic interpretability. As an introduction to this field, we show how researchers have been able to partly reverse-engineer how InceptionV1, a convolutional neural network, recognizes images.
▀▀▀▀▀▀▀▀▀SOURCES & READINGS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
This topic is truly a rabbit hole. If you want to learn more about this important research and even contribute to it, check out this list of sources about mechanistic interpretability and interpretability in general we've compiled for you:
On Interpreting InceptionV1:
Feature visualization: https://distill.pub/2017/feature-visualization/
Zoom in: An Introduction to Circuits: https://distill.pub/2020/circuits/zoom-in/
The Distill journal contains several articles that try to make sense of how exactly InceptionV1 does what it does: https://distill.pub/2020/circuits/
OpenAI's Microscope tool lets us visualize the neurons and channels of a number of vision models in great detail: https://microscope.openai.com/models
Here's OpenAI's Microscope tool pointed on layer Mixed3b in InceptionV1: https://microscope.openai.com/models/inceptionv1/mixed3b_0?models.op.feature_vis.type=channel&models.op.technique=feature_vis
Activation atlases: https://distill.pub/2019/activation-atlas/
More recent work applying SAEs to InceptionV1: https://arxiv.org/abs/2406.03662v1
Transformer Circuits Thread, the spiritual successor of the circuits thread on InceptionV1. This time on transformers: https://transformer-circuits.pub/
In the video, we cite "Toy Models of Superposition": https://transformer-circuits.pub/2022/toy_model/index.html
We also cite "Towards Monosemanticity: Decomposing Language Models With Dictionary Learning": https://transformer-circuits.pub/2023/monosemantic-features/
More recent progress:
Mapping the Mind of a Large Language Model:
Press: https://www.anthropic.com/research/mapping-mind-language-model
Paper in the transformers circuits thread: https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
Extracting Concepts from GPT-4:
Press: https://openai.com/index/extracting-concepts-from-gpt-4/
Paper: https://arxiv.org/abs/2406.04093
Browse features: https://openaipublic.blob.core.windows.net/sparse-autoencoder/sae-viewer/index.html
Language models can explain neurons in language models (cited in the video):
Press: https://openai.com/index/language-models-can-explain-neurons-in-language-models/
Paper: https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html
View neurons: https://openaipublic.blob.core.windows.net/neuron-explainer/neuron-viewer/index.html
Neel Nanda on how to get started with Mechanistic Interpretability:
Concrete Steps to Get Started in Transformer Mechanistic Interpretability: https://www.neelnanda.io/mechanistic-interpretability/getting-started
Mechanistic Interpretability Quickstart Guide: https://www.neelnanda.io/mechanistic-interpretability/quickstart
200 Concrete Open Problems in Mechanistic Interpretability: https://www.alignmentforum.org/posts/LbrPTJ4fmABEdEnLf/200-concrete-open-problems-in-mechanistic-interpretability
More work mentioned in the video:
Progress measures for grokking via mechanistic interpretability: https://arxiv.org/abs/2301.05217
Discovering Latent Knowledge in Language Models Without Supervision: https://arxiv.org/abs/2212.03827
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning: https://www.nature.com/articles/s41551-018-0195-0
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🟠 Patreon: https://www.patreon.com/rationalanimations
🔵 Channel membership: https://www.youtube.com/channel/UCgqt1RE0k0MIr0LoyJRy2lg/join
🟢 Merch: https://rational-animations-shop.fourthwall.com
🟤 Ko-fi, for one-time and recurring donations: https://ko-fi.com/rationalanimations
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Discord: https://discord.gg/RationalAnimations
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▀▀▀▀▀▀▀▀▀PATRONS & MEMBERS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
AAAA you don't fit in the description this time! But we thank you from the bottom of our hearts. All of you, in this Google Doc: https://docs.google.com/document/d/18S3cEkXrllXdWQMxL9G0KjB26YMZnbA4I4VHw5j55oA/edit?usp=sharing
▀▀▀▀▀▀▀CREDITS▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
All the good doggos who worked on this video: https://docs.google.com/document/d/1KQZCfiv1nFKrAm9vcXNjNzQfTLVqY_ofXcWlgXH_dVY/edit?usp=sharing
- published: 14 Jun 2024
- views: 215511
12:23
Neural Networks and Deep Learning: Crash Course AI #3
You can learn more about CuriosityStream at https://curiositystream.com/crashcourse.
Today, we're going to combine the artificial neuron we created last week i...
You can learn more about CuriosityStream at https://curiositystream.com/crashcourse.
Today, we're going to combine the artificial neuron we created last week into an artificial neural network. Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they've become one of the most dominant machine learning technologies used today.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
#CrashCourse #ArtificialIntelligence #MachineLearning
https://wn.com/Neural_Networks_And_Deep_Learning_Crash_Course_Ai_3
You can learn more about CuriosityStream at https://curiositystream.com/crashcourse.
Today, we're going to combine the artificial neuron we created last week into an artificial neural network. Artificial neural networks are better than other methods for more complicated tasks like image recognition, and the key to their success is their hidden layers. We'll talk about how the math of these networks work and how using many hidden layers allows us to do deep learning. Neural networks are really powerful at finding patterns in data which is why they've become one of the most dominant machine learning technologies used today.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
Eric Prestemon, Sam Buck, Mark Brouwer, Timothy J Kwist, Brian Thomas Gossett, Haxiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Zach Van Stanley, Bob Doye, Jennifer Killen, Nathan Catchings, Brandon Westmoreland, dorsey, Indika Siriwardena, Kenneth F Penttinen, Trevin Beattie, Erika & Alexa Saur, Justin Zingsheim, Jessica Wode, Tom Trval, Jason Saslow, Nathan Taylor, Khaled El Shalakany, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, David Noe, Shawn Arnold, William McGraw, Andrei Krishkevich, Rachel Bright, Jirat, Ian Dundore
--
Want to find Crash Course elsewhere on the internet?
Facebook - http://www.facebook.com/YouTubeCrashCourse
Twitter - http://www.twitter.com/TheCrashCourse
Tumblr - http://thecrashcourse.tumblr.com
Support Crash Course on Patreon: http://patreon.com/crashcourse
CC Kids: http://www.youtube.com/crashcoursekids
#CrashCourse #ArtificialIntelligence #MachineLearning
- published: 23 Aug 2019
- views: 353350