-
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
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Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube
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🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=DescriptionFF&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=NNin5Min-bfmFfD2RIc...
published: 19 Jun 2019
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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
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But what is a neural network? | Chapter 1, Deep learning
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 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 Nielsen's way if you get something out of it. And second, i...
published: 05 Oct 2017
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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!
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#MachineLearning #DeepLearning #neuralnetworks
published: 22 Jul 2022
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Gradient descent, how neural networks learn | Chapter 2, Deep learning
Enjoy these videos? Consider sharing one or two.
Help fund future projects: https://www.patreon.com/3blue1brown
Special thanks to these supporters: http://3b1b.co/nn2-thanks
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
This video was supported by Amplify Partners.
For any early-stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com
To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-networks-and-deep-learning
MNIST database:
http://yann.lecun.com/exdb/mnist/
Also check out Chris Olah's blog:
http://colah.git...
published: 16 Oct 2017
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Training Neural Networks: Crash Course AI #4
Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error. Then we’ll send John Green Bot into the metaphorical jungle to find where this error is the smallest, known as the global optimal solution, compared to just where it is relatively small, called local optimal solutions, and we'll discuss some strategies we can use to help neural networks find these optimized solutions more quickly.
Crash Course is produced in association with PBS Digital Studios
https://www.youtube.com/pbsdigitalstudios
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following ...
published: 30 Aug 2019
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Why Neural Networks can learn (almost) anything
A video about neural networks, how they work, and why they're useful.
My twitter: https://twitter.com/max_romana
SOURCES
Neural network playground: https://playground.tensorflow.org/
Universal Function Approximation:
Proof: https://cognitivemedium.com/magic_paper/assets/Hornik.pdf
Covering ReLUs: https://proceedings.neurips.cc/paper/2017/hash/32cbf687880eb1674a07bf717761dd3a-Abstract.html
Covering discontinuous functions: https://arxiv.org/pdf/2012.03016.pdf
Turing Completeness:
Networks of infinite size are turing complete: Neural Computability I & II (behind a paywall unfourtunately, but is cited in following paper)
RNNs are turing complete: https://binds.cs.umass.edu/papers/1992_Siegelmann_COLT.pdf
Transformers are turing complete: https://arxiv.org/abs/2103.05247
More on backpropa...
published: 12 Mar 2022
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Fast statistical inference with neural networks and amortisation: Golden ticket or red herring?
Join the Neural Network!
https://aiforgood.itu.int/neural-network/
The AI for Good networking community platform powered by AI.
Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.
Watch the latest #AIforGood videos!
https://www.youtube.com/c/AIforGood/videos
Stay updated and join our weekly AI for Good newsletter:
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Connect on our social media:
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published: 14 Feb 2024
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Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
This full course video on Neural Network tutorial will help you understand what a neural network is, how it works, and what are the different types of neural networks. You will learn how each neuron processes data, what are activation functions, and how a neuron fires. You will get an idea about backpropagation and gradient descent algorithms. You will have a loo...
published: 02 Jan 2020
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.
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
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: 779005
5:45
Neural Network In 5 Minutes | What Is A Neural Network? | How Neural Networks Work | Simplilearn
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-mach...
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube
🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube
🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=DescriptionFF&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=NNin5Min-bfmFfD2RIcg&utm_medium=DescriptionFF&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
To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: https://www.simplilearn.com/artificial-intelligence-career-guide-pdf?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=Description&utm_source=youtube
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: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=Description&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
https://wn.com/Neural_Network_In_5_Minutes_|_What_Is_A_Neural_Network_|_How_Neural_Networks_Work_|_Simplilearn
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube
🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube
🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=DescriptionFF&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=NNin5Min-bfmFfD2RIcg&utm_medium=DescriptionFF&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
To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Download the Artificial Intelligence Career Guide and take a sneak peek into the world that awaits you: https://www.simplilearn.com/artificial-intelligence-career-guide-pdf?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=Description&utm_source=youtube
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: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=NNin5Min-bfmFfD2RIcg&utm_medium=Description&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
- published: 19 Jun 2019
- views: 1244875
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: 130833
18:40
But what is a neural network? | Chapter 1, Deep learning
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 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 Nielsen's way 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
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_|_Chapter_1,_Deep_Learning
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 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 Nielsen's way 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
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: 15601446
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
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#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
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#MachineLearning #DeepLearning #neuralnetworks
- published: 22 Jul 2022
- views: 166112
20:33
Gradient descent, how neural networks learn | Chapter 2, Deep learning
Enjoy these videos? Consider sharing one or two.
Help fund future projects: https://www.patreon.com/3blue1brown
Special thanks to these supporters: http://3b1b...
Enjoy these videos? Consider sharing one or two.
Help fund future projects: https://www.patreon.com/3blue1brown
Special thanks to these supporters: http://3b1b.co/nn2-thanks
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
This video was supported by Amplify Partners.
For any early-stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com
To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-networks-and-deep-learning
MNIST database:
http://yann.lecun.com/exdb/mnist/
Also check out Chris Olah's blog:
http://colah.github.io/
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.
And if you like that, you'll *love* the publications at distill:
https://distill.pub/
For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs
"But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville.
Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:
https://arxiv.org/abs/1611.03530
https://arxiv.org/abs/1706.05394
https://arxiv.org/abs/1412.0233
Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
Thanks to these viewers for their contributions to translations
Hebrew: Omer Tuchfeld
Italian: @teobucci
-------------------
Video timeline
0:00 - Introduction
0:30 - Recap
1:49 - Using training data
3:01 - Cost functions
6:55 - Gradient descent
11:18 - More on gradient vectors
12:19 - Gradient descent recap
13:01 - Analyzing the network
16:37 - Learning more
17:38 - Lisha Li interview
19:58 - Closing thoughts
------------------
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/Gradient_Descent,_How_Neural_Networks_Learn_|_Chapter_2,_Deep_Learning
Enjoy these videos? Consider sharing one or two.
Help fund future projects: https://www.patreon.com/3blue1brown
Special thanks to these supporters: http://3b1b.co/nn2-thanks
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks
This video was supported by Amplify Partners.
For any early-stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com
To learn more, I highly recommend the book by Michael Nielsen
http://neuralnetworksanddeeplearning.com/
The book walks through the code behind the example in these videos, which you can find here:
https://github.com/mnielsen/neural-networks-and-deep-learning
MNIST database:
http://yann.lecun.com/exdb/mnist/
Also check out Chris Olah's blog:
http://colah.github.io/
His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great.
And if you like that, you'll *love* the publications at distill:
https://distill.pub/
For more videos, Welch Labs also has some great series on machine learning:
https://youtu.be/i8D90DkCLhI
https://youtu.be/bxe2T-V8XRs
"But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville.
Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end:
https://arxiv.org/abs/1611.03530
https://arxiv.org/abs/1706.05394
https://arxiv.org/abs/1412.0233
Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
Thanks to these viewers for their contributions to translations
Hebrew: Omer Tuchfeld
Italian: @teobucci
-------------------
Video timeline
0:00 - Introduction
0:30 - Recap
1:49 - Using training data
3:01 - Cost functions
6:55 - Gradient descent
11:18 - More on gradient vectors
12:19 - Gradient descent recap
13:01 - Analyzing the network
16:37 - Learning more
17:38 - Lisha Li interview
19:58 - Closing thoughts
------------------
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: 16 Oct 2017
- views: 6461857
12:29
Training Neural Networks: Crash Course AI #4
Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks b...
Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error. Then we’ll send John Green Bot into the metaphorical jungle to find where this error is the smallest, known as the global optimal solution, compared to just where it is relatively small, called local optimal solutions, and we'll discuss some strategies we can use to help neural networks find these optimized solutions more quickly.
Crash Course is produced in association with PBS Digital Studios
https://www.youtube.com/pbsdigitalstudios
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/Training_Neural_Networks_Crash_Course_Ai_4
Today we’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error. Then we’ll send John Green Bot into the metaphorical jungle to find where this error is the smallest, known as the global optimal solution, compared to just where it is relatively small, called local optimal solutions, and we'll discuss some strategies we can use to help neural networks find these optimized solutions more quickly.
Crash Course is produced in association with PBS Digital Studios
https://www.youtube.com/pbsdigitalstudios
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: 30 Aug 2019
- views: 179295
10:30
Why Neural Networks can learn (almost) anything
A video about neural networks, how they work, and why they're useful.
My twitter: https://twitter.com/max_romana
SOURCES
Neural network playground: https://pl...
A video about neural networks, how they work, and why they're useful.
My twitter: https://twitter.com/max_romana
SOURCES
Neural network playground: https://playground.tensorflow.org/
Universal Function Approximation:
Proof: https://cognitivemedium.com/magic_paper/assets/Hornik.pdf
Covering ReLUs: https://proceedings.neurips.cc/paper/2017/hash/32cbf687880eb1674a07bf717761dd3a-Abstract.html
Covering discontinuous functions: https://arxiv.org/pdf/2012.03016.pdf
Turing Completeness:
Networks of infinite size are turing complete: Neural Computability I & II (behind a paywall unfourtunately, but is cited in following paper)
RNNs are turing complete: https://binds.cs.umass.edu/papers/1992_Siegelmann_COLT.pdf
Transformers are turing complete: https://arxiv.org/abs/2103.05247
More on backpropagation:
https://www.youtube.com/watch?v=Ilg3gGewQ5U
More on the mandelbrot set:
https://www.youtube.com/watch?v=NGMRB4O922I
Additional Sources:
Neat explanation of universal function approximation proof: https://www.youtube.com/watch?v=Ijqkc7OLenI
Where I got the hard coded parameters: https://towardsdatascience.com/can-neural-networks-really-learn-any-function-65e106617fc6
Reviewers:
Andrew Carr https://twitter.com/andrew_n_carr
Connor Christopherson
TIMESTAMPS
(0:00) Intro
(0:27) Functions
(2:31) Neurons
(4:25) Activation Functions
(6:36) NNs can learn anything
(8:31) NNs can't learn anything
(9:35) ...but they can learn a lot
MUSIC
https://www.youtube.com/watch?v=SmkUY_B9fGg
https://wn.com/Why_Neural_Networks_Can_Learn_(Almost)_Anything
A video about neural networks, how they work, and why they're useful.
My twitter: https://twitter.com/max_romana
SOURCES
Neural network playground: https://playground.tensorflow.org/
Universal Function Approximation:
Proof: https://cognitivemedium.com/magic_paper/assets/Hornik.pdf
Covering ReLUs: https://proceedings.neurips.cc/paper/2017/hash/32cbf687880eb1674a07bf717761dd3a-Abstract.html
Covering discontinuous functions: https://arxiv.org/pdf/2012.03016.pdf
Turing Completeness:
Networks of infinite size are turing complete: Neural Computability I & II (behind a paywall unfourtunately, but is cited in following paper)
RNNs are turing complete: https://binds.cs.umass.edu/papers/1992_Siegelmann_COLT.pdf
Transformers are turing complete: https://arxiv.org/abs/2103.05247
More on backpropagation:
https://www.youtube.com/watch?v=Ilg3gGewQ5U
More on the mandelbrot set:
https://www.youtube.com/watch?v=NGMRB4O922I
Additional Sources:
Neat explanation of universal function approximation proof: https://www.youtube.com/watch?v=Ijqkc7OLenI
Where I got the hard coded parameters: https://towardsdatascience.com/can-neural-networks-really-learn-any-function-65e106617fc6
Reviewers:
Andrew Carr https://twitter.com/andrew_n_carr
Connor Christopherson
TIMESTAMPS
(0:00) Intro
(0:27) Functions
(2:31) Neurons
(4:25) Activation Functions
(6:36) NNs can learn anything
(8:31) NNs can't learn anything
(9:35) ...but they can learn a lot
MUSIC
https://www.youtube.com/watch?v=SmkUY_B9fGg
- published: 12 Mar 2022
- views: 1137119
1:16:49
Fast statistical inference with neural networks and amortisation: Golden ticket or red herring?
Join the Neural Network!
https://aiforgood.itu.int/neural-network/
The AI for Good networking community platform powered by AI.
Designed to help users build...
Join the Neural Network!
https://aiforgood.itu.int/neural-network/
The AI for Good networking community platform powered by AI.
Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.
Watch the latest #AIforGood videos!
https://www.youtube.com/c/AIforGood/videos
Stay updated and join our weekly AI for Good newsletter:
http://eepurl.com/gI2kJ5
Check out the latest AI for Good news:
https://aiforgood.itu.int/newsroom/
Explore the AI for Good blog:
https://aiforgood.itu.int/ai-for-good-blog/
Connect on our social media:
Website: https://aiforgood.itu.int/
Twitter: https://twitter.com/AIforGood
LinkedIn Page: https://www.linkedin.com/company/26511907
LinkedIn Group: https://www.linkedin.com/groups/8567748
Instagram: https://www.instagram.com/aiforgood
Facebook: https://www.facebook.com/AIforGood
What is AI for Good?
We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.
More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.
AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.
Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
https://wn.com/Fast_Statistical_Inference_With_Neural_Networks_And_Amortisation_Golden_Ticket_Or_Red_Herring
Join the Neural Network!
https://aiforgood.itu.int/neural-network/
The AI for Good networking community platform powered by AI.
Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.
Watch the latest #AIforGood videos!
https://www.youtube.com/c/AIforGood/videos
Stay updated and join our weekly AI for Good newsletter:
http://eepurl.com/gI2kJ5
Check out the latest AI for Good news:
https://aiforgood.itu.int/newsroom/
Explore the AI for Good blog:
https://aiforgood.itu.int/ai-for-good-blog/
Connect on our social media:
Website: https://aiforgood.itu.int/
Twitter: https://twitter.com/AIforGood
LinkedIn Page: https://www.linkedin.com/company/26511907
LinkedIn Group: https://www.linkedin.com/groups/8567748
Instagram: https://www.instagram.com/aiforgood
Facebook: https://www.facebook.com/AIforGood
What is AI for Good?
We have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.
More than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.
AI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.
Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
- published: 14 Feb 2024
- views: 117
3:17:22
Neural Network Full Course | Neural Network Tutorial For Beginners | Neural Networks | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-ob1yS9g-Zcs&...
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
This full course video on Neural Network tutorial will help you understand what a neural network is, how it works, and what are the different types of neural networks. You will learn how each neuron processes data, what are activation functions, and how a neuron fires. You will get an idea about backpropagation and gradient descent algorithms. You will have a look at the convolution neural network and how it identifies objects in an image. Finally, you will understand about the recurrent neural networks and lstm in detail. Now, let's get started with learning neural networks.
🔥Free AI Course With Course Completion Certificate: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
Dataset Link - https://drive.google.com/drive/folders/11T76B8UkTg9lU-sPhPlWqn6MOVhQ-FjS
Below topics are explained in this Neural Network Full Course:
1. Animated Video 00:52
2. What is A Neural Network 06:35
3. What is Deep Learning 07:40
4. What is Artificial Neural Network 09:00
5. How Does Neural Network Works 10:37
6. Advantages of Neural Network 13:39
7. Applications of Neural Network 14:59
8. Future of Neural Network 17:03
9. How Does Neural Network Works 19:10
10. Types of Artificial Neural Network 29:27
11. Use Case-Problem Statement 34:57
12. Use Case-Implementation 36:17
13. Backpropagation & Gradient Descent 01:06:00
14. Loss Fubction 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
17. Convolutional Neural Network 01:17:54
18. How Image recognition Works 01:17:58
19. Introduction to CNN 01:20:25
20. What is Convolutional Neural Network 01:20:51
21. How CNN recognize Images 01:25:34
22. Layers in Convolutional Neural Network 01:26:19
23. Use Case implementation using CNN 01:39:21
24. What is a Neural Network 02:21:24
25. Popular Neural Network 02:23:08
26. Why Recurrent Neural Network 02:24:19
27. Applications of Recurrent Neural Network 02:25:32
28. how does a RNN works 02:28:42
29. vanishing And Exploding Gradient Problem 02:31:02
30. Long short term Memory 02:35:54
31. use case implementation of LSTM 02:44:32
To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#NeuralNetwork #NeuralNetworkFullCourse #NeuralNetworkTutorial #WhatIsNeuralNetwork #DeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn
Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=Neural-Network-Full-Course-ob1yS9g-Zcs&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn’s courses, visit:
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Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
https://wn.com/Neural_Network_Full_Course_|_Neural_Network_Tutorial_For_Beginners_|_Neural_Networks_|_Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
🔥Professional Certificate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=AI-ob1yS9g-Zcs&utm_medium=DescriptionFirstFold&utm_source=youtube
This full course video on Neural Network tutorial will help you understand what a neural network is, how it works, and what are the different types of neural networks. You will learn how each neuron processes data, what are activation functions, and how a neuron fires. You will get an idea about backpropagation and gradient descent algorithms. You will have a look at the convolution neural network and how it identifies objects in an image. Finally, you will understand about the recurrent neural networks and lstm in detail. Now, let's get started with learning neural networks.
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Below topics are explained in this Neural Network Full Course:
1. Animated Video 00:52
2. What is A Neural Network 06:35
3. What is Deep Learning 07:40
4. What is Artificial Neural Network 09:00
5. How Does Neural Network Works 10:37
6. Advantages of Neural Network 13:39
7. Applications of Neural Network 14:59
8. Future of Neural Network 17:03
9. How Does Neural Network Works 19:10
10. Types of Artificial Neural Network 29:27
11. Use Case-Problem Statement 34:57
12. Use Case-Implementation 36:17
13. Backpropagation & Gradient Descent 01:06:00
14. Loss Fubction 01:10:26
15. Gradient Descent 01:11:26
16. Backpropagation 01:13:07
17. Convolutional Neural Network 01:17:54
18. How Image recognition Works 01:17:58
19. Introduction to CNN 01:20:25
20. What is Convolutional Neural Network 01:20:51
21. How CNN recognize Images 01:25:34
22. Layers in Convolutional Neural Network 01:26:19
23. Use Case implementation using CNN 01:39:21
24. What is a Neural Network 02:21:24
25. Popular Neural Network 02:23:08
26. Why Recurrent Neural Network 02:24:19
27. Applications of Recurrent Neural Network 02:25:32
28. how does a RNN works 02:28:42
29. vanishing And Exploding Gradient Problem 02:31:02
30. Long short term Memory 02:35:54
31. use case implementation of LSTM 02:44:32
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- published: 02 Jan 2020
- views: 282602