Machine learning is awesome. Who doesn't want to build a cool AI that you can teach to do anything. The only problem is machine learning is very confusing. In this video I breakdown what a neural network is, how you can create one, and how to train it. By the end of this video you will have a fully functional AI.


📚 Materials/References:

GitHub Code: https://github.com/WebDevSimpl....ified/Machine-Learni
Brain.js Library: https://brain.js.org

🧠 Concepts Covered:

- How to use brain.js
- What a neural network is
- How neural networks work
- How to train and use a neural network


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#MachineLearning #WDS #JavaScript

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Years of work down the drain, the convolutional neural network is a step change in image classification accuracy. Image Analyst Dr Mike Pound explains what it does.

Kernel Convolutions: https://youtu.be/C_zFhWdM4ic
Deep Learning: https://youtu.be/l42lr8AlrHk
Botnets: https://youtu.be/UVFmC178_Vs
AI's Game Playing Challenge: https://youtu.be/5oXyibEgJr0
Space Carving: https://youtu.be/cGs90KF4oTc

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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: http://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

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/e....n/blogs/2018/EiaM-Ne

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.

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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

Today we're going to talk big picture about what Neural Networks are and how they work. Neural Networks, which are computer models that act like neurons in the human brain, are really popular right now - they're being used in everything from self-driving cars and Snapchat filters to even creating original art! As data gets bigger and bigger neural networks will likely play an increasingly important role in helping us make sense of all that data.

Special thanks to Max Deutsch giving us permission to use his Neural Network-written Harry Potter chapter in this episode! Read more about his work here:
https://medium.com/deep-writin....g/harry-potter-writt

Neural Network to create anime characters:
https://arxiv.org/pdf/1708.05509.pdf

Neural Network to great Van Gogh-like art:
https://towardsdatascience.com..../gangogh-creating-ar

Neural Network to create skate decks:
https://github.com/RJT1990/gandecks/

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Kaggle notebook with all the code: https://www.kaggle.com/wwsalmo....n/simple-mnist-nn-fr

Blog article with more/clearer math explanation: https://www.samsonzhang.com/20....20/11/24/understandi

Just what is happening inside a Convolutional Neural Network? Dr Mike Pound shows us the images in between the input and the result.

How Blurs & Filters Work (Kernel Convolutions): https://youtu.be/C_zFhWdM4ic
Cookie Stealing: https://youtu.be/T1QEs3mdJoc
Rob Miles on Game Playing AI: https://youtu.be/5oXyibEgJr0
Secure Web Browsing: https://www.youtube.com/watch?v=E_wX40fQwEA
Deep Learning: https://youtu.be/l42lr8AlrHk


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This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: http://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

In this project I built a neural network and trained it to play Snake using a genetic algorithm.

Thanks for watching! Subscribe if you enjoyed and Share if you know anyone who would be interested!

GitHub Repo: https://github.com/greerviau/SnakeAI
Twitter: https://twitter.com/greerviau
Support me on Patreon: https://www.patreon.com/greerviau

Thanks to Josh Cominelli for the music!
Soundcloud: https://soundcloud.com/josh-cominelli

Exploring how neural networks learn by programming one from scratch in C#, and then attempting to teach it to recognize various doodles and images.

Source code: https://github.com/SebLague/Ne....ural-Network-Experim
Demo: https://sebastian.itch.io/neur....al-network-experimen

If you'd like to support me in creating more videos (and get early access to new stuff), you can join my patreon here: https://www.patreon.com/SebastianLague

The data is from:
Digits http://yann.lecun.com/exdb/mnist/
Fashion https://github.com/zalandoresearch/fashion-mnist
Doodles https://github.com/googlecreat....ivelab/quickdraw-dat
Cifar10 https://www.cs.toronto.edu/~kriz/cifar.html

Chapters
0:00 Introduction
2:39 The decision boundary
3:49 Weights
5:42 Biases
6:45 Hidden layers
7:45 Programming the network
9:57 Activation functions
12:42 Cost
15:07 Gradient descent example
18:22 The cost landscape
19:55 Programming gradient descent
21:10 It's learning! (slowly)
23:21 Calculus example
27:34 The chain rule
29:50 Some partial derivatives
33:14 Backpropagation
39:25 Digit recognition
43:56 Drawing our own digits
47:37 Fashion
48:25 Doodles
52:00 The final challenge

Music:
Cosmic Waves - Michael FK
Amber - The Stolen Orchestra
Beyond the Horizon - Sounds Like Sander
Air - Assaf Ayalon
Purest Form - Sounds Like Sander
Hear Wide Open - Sounds Like Sander
Universal Wonder - Moments
Roman P - Moments
All In Good Time - Shimmer
It Will Come Back - The Stolen Orchestra
Frontier - Shimmer
New Moon - Cloud Wave
Sunflower - Cody Martin
Inner Peace - Moments
Enchanted - Cody Martin
Just Around The Corner - Shimmer

If you know nothing about how a neural network works, this is the video for you! I've worked for weeks to find ways to explain this in a way that is easy to understand for beginners.


Past Videos:
Intro to Machine Learning with Javascript:
https://www.youtube.com/watch?v=9Hz3P1VgLz4&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=2&t=0s

Machine Learning 2 - Building a Recommendation Engine:
https://www.youtube.com/watch?v=lvzekeBQsSo&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=3&t=0s

Machine learning and neural networks are awesome. This video provides beginners with an easy tutorial explaining how a neural network works - what math is involved, and a step by step explanation of how the data moves through the network.

The example used will be a feed forward neural network with back propagation. It explains the difference between linear and non linear data, the importance of the activation function, learning rate, and momentum configurations.

-~-~~-~~~-~~-~-
Also watch: "Tailwind CSS - why CSS utility classes save so much time"
https://www.youtube.com/watch?v=oU5ar0dmQEY
-~-~~-~~~-~~-~-

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/ma....gic_paper/assets/Hor
Covering ReLUs: https://proceedings.neurips.cc..../paper/2017/hash/32c
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/pap....ers/1992_Siegelmann_
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

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

Neural Networks → https://ibm.biz/Neural_Networks_Now
Let's Create Data Fabric Instead of Data Silos → https://ibm.biz/Your_data_fabric

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.

Download a free AI ebook → https://ibm.biz/Free_AI_Ebook_Today

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#Software #ITModernization #NeuralNetworks #DataFabric #lightboard #IBM

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Hi data nerds! Today I’m sharing with you my recommended learning roadmap and the courses to help you learn the core data science skills quickly. Resources are abundant online, but it is hard sometimes to select what is right for you, and what you really need. I hope with this learning roadmap, you can learn the most in-demand skills in the most effective way. Hope you enjoy it!

Thank you for watching and have a great day! 🤗

🔑 TIMESTAMPS
================================
0:00 - Intro
0:17 - My data science journey 🤓
2:12 - Google Data Analytics Professional cert
4:14 - Learn SQL Basic for DS Specialization
6:37 - Applied Data Science with Python Specialization
8:34 - Mathematics for Machine Learning Specialisation
10:50 - Data Visualisation with Tableau Specialisation
13:45 - Tips for saving $$


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#datascience #machinelearning #DataAnalysis #Datanerd #ThuVu #dataanalytics

In this video I will give you the resources you need to learn data science from zero knowledge. We will discuss several programming books and math books that are perfect for beginners who want to acquire the skills to become a data scientist. In particular we will look at books on R, Python, Calculus, Linear Algebra, and Statistics. Several more advanced books are also presented in this video. Do you have any other book recommendations for learning Data Science? If so, please leave a comment below.

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Notes of this Lecture: