This is a report of a software project that created the conditions for evolution in an attempt to learn something about how evolution works in nature. This is for the programmer looking for ideas for interdisciplinary programming projects, or for anyone interested in how evolution and natural selection work.

Before commenting on the religious/theological implications of this simulation, please note that this video in no way purports to explain all the mysteries of life and the universe.

GitHub: https://github.com/davidrmiller/biosim4

Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. This StatQuest focuses on explaining the main ideas in a way that is easy to understand.

NOTE: This StatQuest assumes that you already know the main ideas behind...
Neural Networks: https://youtu.be/CqOfi41LfDw
The Chain Rule: https://youtu.be/wl1myxrtQHQ
Gradient Descent: https://youtu.be/sDv4f4s2SB8

LAST NOTE: When I was researching this 'Quest, I found this page by Sebastian Raschka to be helpful: https://sebastianraschka.com/f....aq/docs/backprop-arb

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/channe....l/UCtYLUTtgS3k1Fg4y5

...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/s....tatquest-with-josh-s

...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
3:55 Fitting the Neural Network to the data
6:04 The Sum of the Squared Residuals
7:23 Testing different values for a parameter
8:38 Using the Chain Rule to calculate a derivative
13:28 Using Gradient Descent
16:05 Summary

#StatQuest #NeuralNetworks #Backpropagation

This video uses a spatial analogy to explore why deep neural networks are more powerful than shallow ones. This is part 4 in my deep learning series: https://www.youtube.com/playli....st?list=PLbg3ZX2pWlg We'll explore what neurons are doing individually and as a group to "understand" perceptions. It leads us to the Manifold Hypothesis.

Ms. Coffee Bean appears with the definitive introduction to Graph Neural Networks! Or short: GNNs. Because graphs are everywhere (almost).

▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
🔥 Optionally, pay us a coffee to boost our Coffee Bean production! ☕
Patreon: https://www.patreon.com/AICoffeeBreak
Ko-fi: https://ko-fi.com/aicoffeebreak
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀

Outline:
* 00:00 Graphs are everywhere!
* 02:32 GNNs explained
* 07:25 GNNs applications

🔗 Links:
YouTube: https://www.youtube.com/AICoffeeBreak
Twitter: https://twitter.com/AICoffeeBreak
Reddit: https://www.reddit.com/r/AICoffeeBreak/

#AICoffeeBreak #MsCoffeeBean #GCN

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers
❤️ Their mentioned post is available here: https://wandb.ai/wandb/in-betw....een/reports/-Overvie

📝 The paper "Robust Motion In-betweening" is available here:
- https://static-wordpress.akama....ized.net/montreal.ub
- https://montreal.ubisoft.com/e....n/automatic-in-betwe

Dataset: https://github.com/XefPatterso....n/Ubisoft-LaForge-An

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, Joshua Goller, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh.
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Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://discordapp.com/invite/hbcTJu2

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Web: https://cg.tuwien.ac.at/~zsolnai/

#gamedev

After my last video I got a lot of comments (mainly on Reddit) asking me to make a video explaining how I did it.
It took me a while to learn how to video edit, voice act, and animate, so it was about time I presented and explained this project.

The Bibites

Made in C# on Unity

I highly inspired my algorithm from the following document :
Stanley K. O. and Miikkulainen R. (2002). Evolving Neural
Networks through Augmenting Topologies. MIT Press journals

Music: "Perspectives" by Kevin MacLeod
http://incompetech.com/music/royalty-...

#minecraft #neuralnetwork #backpropagation

I built an analog neural network in vanilla Minecraft without any mods or command blocks. The network uses Redstone wire power strengths to carry the signal through one hidden layer, including nonlinearities, and then do automatic backpropagation and even weight updates.

OUTLINE:
0:00 - Intro & Overview
1:50 - Redstone Components Explained
5:00 - Analog Multiplication in Redstone
7:00 - Gradient Descent for Square Root Computation
9:35 - Neural Network Demonstration
10:45 - Network Schema Explained
18:35 - The Network Learns a Datapoint
20:20 - Outro & Conclusion

I built this during a series of live streams and want to thank everyone who helped me and cheered for me in the chat!

World saves here: https://github.com/yk/minecraft-neural-network
Game here: https://www.minecraft.net
Multiplier Inspiration: https://www.youtube.com/channe....l/UCLmzk4TlnLXCXCHcj

Credits to Lanz for editing!

Links:
TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick
YouTube: https://www.youtube.com/c/yannickilcher
Twitter: https://twitter.com/ykilcher
Discord: https://discord.gg/4H8xxDF
BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
Parler: https://parler.com/profile/YannicKilcher
LinkedIn: https://www.linkedin.com/in/ya....nnic-kilcher-4885341
BiliBili: https://space.bilibili.com/1824646584

If you want to support me, the best thing to do is to share out the content :)

If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: https://www.subscribestar.com/yannickilcher
Patreon: https://www.patreon.com/yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Can we measure memories in networks of neurons in bytes? Or should we think of our memory differently?

Submission to the Summer of Math Exposition 2022 (#SoME2). More information: https://summerofmathexposition.....substack.com/p/the-

Time stamps:
0:00 - Where is your memory?
1:41 - Computer memory in a nutshell
2:58 - Modeling neural networks
4:42 - Memories in dynamical systems
9:54 - Learning
13:36 - Memory capacity and conclusion

Animations largely made using the manim community edition:
https://www.manim.community/

Original Paper on Hopfield Networks:
Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.

Neuron image by Santiago Ramón y Cajal, The pyramidal neuron of the cerebral cortex, 1904 Ink and pencil on paper, 8 5/8 x 6 7/8 in. Credit: Cajal Institute (CSIC), Madrid

Music: Aakash Gandhi - "Dreamland"

Can we make neural networks using light? From spatial light modulators to phase-change materials, we're diving into optical neural networks. Sign up for CuriosityStream and Nebula at https://curiositystream.com/jordan to get access to the next journal club on optical neural networks!

Twitter - http://twitter.com/jordanbharrod

Instagram - http://www.instagram.com/jordanbharrod

MY GEAR (Affiliate Link): https://www.amazon.com/shop/jordanharrod

For business inquiries, contact [email protected]

Sources:

Cheng, T. Y., Chou, D. Y., Liu, C. C., Chang, Y. J., & Chen, C. C. (2019). Optical neural networks based on optical fiber-communication system. Neurocomputing, 364, 239–244. https://doi.org/10.1016/j.neucom.2019.07.051

Sui, X., Wu, Q., Liu, J., Chen, Q., & Gu, G. (2020). A review of optical neural networks. IEEE Access, 8, 70773–70783. https://doi.org/10.1109/ACCESS.2020.2987333

Zhou, T., Fang, L., Yan, T., Wu, J., Li, Y., Fan, J., … Dai, Q. (2020). In situ optical backpropagation training of diffractive optical neural networks. Photonics Research, 8(6), 940. https://doi.org/10.1364/prj.389553

Lin, X., Rivenson, Y., Yardimci, N. T., Veli, M., Luo, Y., Jarrahi, M., & Ozcan, A. (2018). All-optical machine learning using diffractive deep neural networks. Science, 361(6406), 1004–1008. https://doi.org/10.1126/science.aat8084

Lu, T. T. (1990). Self-organizing optical neural network for unsupervised learning. Optical Engineering, 29(9), 1107. https://doi.org/10.1117/12.55702

Casasent, D. P., & Barnard, E. (1990). Adaptive-clustering optical neural net. Applied Optics, 29(17), 2603. https://doi.org/10.1364/ao.29.002603

Abu-Mostafa, Y., & Psaltis, D. (1987). Optical Neural Computers. Scientific American, 256(3). https://doi.org/10.2307/24979343

Wilson, C. L., Watson, C. I., & Paek, E. G. (2000). Effect of resolution and image quality on combined optical and neural network fingerprint matching. Pattern Recognition, 33(2), 317–331. https://doi.org/10.1016/S0031-3203(99)00052-7

Lee, L.-S., Stoll, H. M., & Tackitt, M. C. (1989). Continuous-time optical neural network associative memory. Optics Letters, 14(3), 162. https://doi.org/10.1364/ol.14.000162

Bergeron, A., Arsenault, H. H., Eustache, E., & Gingras, D. (1994). Optoelectronic thresholding module for winner-take-all operations in optical neural networks. Applied Optics, 33(8), 1463. https://doi.org/10.1364/ao.33.001463

Caulfield, H. J., Kinser, J., & Rogers, S. K. (1989). Optical Neural Networks. Proceedings of the IEEE, 77(10), 1573–1583. https://doi.org/10.1109/5.40669

Shariv, I., & Friesem, A. A. (1989). All-optical neural network with inhibitory neurons. Optics Letters, 14(10), 485. https://doi.org/10.1364/ol.14.000485

https://qz.com/852770/theres-a....-limit-to-how-small-

https://ieeexplore.ieee.org/document/9064516

https://www.nature.com/articles/s41586-019-1157-8

https://iopscience.iop.org/art....icle/10.1088/2040-89

https://www.osapublishing.org/....optica/abstract.cfm?

https://pixabay.com/videos/cas....tle-church-tower-cit

https://pixabay.com/videos/lig....hts-blinking-abstrus

FCC: This video is sponsored by CuriosityStream

There is a better way to understand how AIs sort data, process images, and make decisions!

Made for the 2021 Summer of Math Exposition: https://www.3blue1brown.com/blog/some1

Source code available here: https://gitlab.com/samsartor/nn_vis

The background music is an excerpt of the endless ambient generative music system "At Sunrise," available at generative.fm/music/alex-bainter-at-sunrise

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers

The shown blog post is available here:
https://www.wandb.com/articles..../visualize-xgboost-i

📝 The paper "Zoom In: An Introduction to Circuits" is available here:
https://distill.pub/2020/circuits/zoom-in/

Followup article: https://distill.pub/2020/circuits/early-vision/

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Daniel Hasegan, Eric Haddad, Eric Martel, Javier Bustamante, Lorin Atzberger, Lukas Biewald, Marcin Dukaczewski, Michael Albrecht, Nader S., Owen Campbell-Moore, Rob Rowe, Robin Graham, Steef, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh
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Web: https://cg.tuwien.ac.at/~zsolnai/

#ai #machinelearning

CNNs for deep learning
Included in Machine Leaning / Deep Learning for Programmers Playlist:
https://www.youtube.com/playli....st?list=PLZbbT5o_s2x

Convolution demo on real data:
https://youtu.be/vJiZqZRkIg8

In this video, we explain the concept of convolutional neural networks, how they're used, and how they work on a technical level. We also discuss the details behind convolutional layers and filters.

fast.ai lesson 4:
http://course17.fast.ai/lessons/lesson4.html

🕒🦎 VIDEO SECTIONS 🦎🕒

00:00 Welcome to DEEPLIZARD - Go to deeplizard.com for learning resources
00:30 See convolution demo on real data - Link in the description
08:07 Collective Intelligence and the DEEPLIZARD HIVEMIND

💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥

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👉 Check out the website for more learning material:
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🧠 Support collective intelligence, join the deeplizard hivemind:
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👀 CHECK OUT OUR VLOG:
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❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind:
Tammy
Mano Prime
Ling Li

🚀 Boost collective intelligence by sharing this video on social media!

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❤️ Please use the knowledge gained from deeplizard content for good, not evil.

Welcome back to TradeSmart!

DISCLAIMER: Someone pointed out that this script might be repainting. Basically that means we can't use this script to get REAL results. However I checked the signals for repaint and couldn't prove that it's repainting. Until further notice take into consideration this possible problem.

In this video I am going to present you the New Best Performing Strategy! First I will I will talk about some channel updates, after that I will show you how to trade the strategy, then the 100 backtest and the results. At the end of the video I will share some final thoughts.

Info about the upcoming Discord channel and the Premium Script giveaway: The Discord channel will be launched next week. To become a member you will have to pay a nominal 5$ (this way we can easily avoid bots and you are also supporting our work) In the Discord channel we will have several chats for different type of conversations like: day trading, swing trading, investing, trading scripts and best settings updates, trading automation, trading competitions and more. By becoming a discord member you will have the opportunity to talk with me and the 2 other TradeSmart team members (strategy optimization using computing power and strategy coders).
Premium Trading Script Giveaway: Between the first 50 Discord members we will give away 1 Premium Script to 10 member, in wich you can access to a wide variety of optimization features which I developed in the last few months. With the Premium Script you will also have the option to connect it directly to your broker/exchange and fully automate trading based on the scripts signals. (We will also give you a detailed walkthrough of how to connect the automation feature to your broker/exchange, and how to utilize the different optimization filters.)

Thank you all for your continious support!
Links for the strategy ranking sheet, the cheat sheet and for our free trading script are down below!

To support our work don't forget to drop a like for the youtube algorithm, subscribe and hit the notification bell, so you wont miss our next video. Thank you!
If you have any ideas or strategy recommendations don't forget to share it with me, down in the description.

We have just launched our Discord channel! If you want to join, first go to our Patreon page and become a tier member. Then follow the instructions and you're done.
Our Patreon: https://www.patreon.com/tradesmart224

Check out the TradeSmart Team and Discord Introduction video here: https://www.youtube.com/watch?v=UGgWihIKRlU
You can also get access to our Premium Scripts and tutorial videos by becoming a Smart Trader Tier member.
Thank you all for your support and see you in the Discord channel!😊

Links:
--------------------------------------------------------
Strategy ranking sheet:
https://docs.google.com/spreadsheets/...

Breakeven cheat sheet:
https://docs.google.com/spreadsheets/...

Our FREE trading Scripts:
https://www.tradingview.com/u/TradeSm...

--------------------------------------------------------
Other Strategy Backtesting Videos:

#1 Free Trading SCRIPT Release! [73% Winrate] [MACD + CMF + EMA + Supertrend Strategy]: https://www.youtube.com/watch?v=ZbYYr...

I Created a 114% Profit Day Trading Strategy Based On My Subscribers Votes
https://www.youtube.com/watch?v=zfknQyIr1ao&t=11s

SIMPLE and PROFITABLE BITCOIN TRADING STRATEGY (MACD + 200EMA) [Tested 300+ Times]: https://www.youtube.com/watch?v=-cH4E...

Simple MACD + 200 EMA Trading Strategy Tested 900x Times: https://www.youtube.com/watch?v=JbqoJ...

Profitable Swing Trading Strategy [Tested 100 Times & How to Optimize for Better Results]: https://www.youtube.com/watch?v=u_D1R...

70% WINRATE STRATEGY TESTED 100x (MACD + PARABOLIC SAR + 200 EMA): https://www.youtube.com/watch?v=4uyPR...

A gentle intro to neural networks.

Perceptron Video : https://www.youtube.com/watch?v=4Gac5I64LM4

Logistic Regression Video : https://www.youtube.com/watch?v=9zw76PT3tzs

My Patreon : https://www.patreon.com/user?u=49277905

Learn more about CNNs → http://ibm.biz/cnn-guide
Learn more about Neural Networks → http://ibm.biz/neural-networks-guide
Check out IBM Watson Studio → http://ibm.biz/prod-ibm-watson-studio

Convolutional neural networks, or CNNs, are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. But how exactly do they work?

In this lightboard video, Martin Keen with IBM, explains how this deep learning algorithm operates to enable machines to view the world as humans do.

Get started on IBM Cloud at no cost → http://ibm.biz/free-acct-creation
Subscribe to see more videos like this in the future → http://ibm.biz/subscribe-now​​

#ConvolutionalNeuralNetworks #Neural Networks #AI

Visuals to demonstrate how a neural network classifies a set of data. Thanks for watching!
Support me on Patreon! https://patreon.com/vcubingx
Source Code: https://github.com/vivek3141/dl-visualization

Here's the course I referred to in the video. I am not affiliated with NYU.
https://www.youtube.com/playli....st?list=PLLHTzKZzVU9

Sinusoids as activation functions:
https://openreview.net/forum?id=Sks3zF9eg
https://vsitzmann.github.io/siren/

Here's the distill.pub article:
https://distill.pub/2020/grand-tour/

Special thanks to Alfredo Canziani and Nikhil Maserang for reviewing the video.

And also thanks to Grant Sanderson himself for giving me some manim tips!

I've been active on twitter, follow me here!
https://twitter.com/vcubingx

Join my discord server!
https://discord.gg/Kj8QUZU

These animation in this video was made using 3blue1brown's library, manim:
https://github.com/3b1b/manim

Music is from GameChops (Route 113, Azalea Town, Ecruteak City

Follow me!
Website: https://vcubingx.com
Twitter: https://twitter.com/vcubingx
Github: https://github.com/vivek3141
Instagram: https://instagram.com/vcubingx
Patreon: https://patreon.com/vcubingx

What does a Neural Network *actually* do? Visualizing Deep Learning, Chapter 2

0:00 Intro
0:18 Recap of Part 1
1:57 Introducing the dataset
2:52 Structure of the Neural Network we’ll be using
3:34 What is softmax?
5:52 Input space decision boundaries
6:24 Modifying the Neural Network to visualize what it’s doing
7:36 Out-of-domain boundaries
8:46 sin(x) as an activation function
9:30 Neuron planes
11:57 Softmax surfaces
13:20 MNIST Transformation
13:42 Outro