Evolutionary computation | Evolutionary algorithms | Artificial neural networks | Genetic algorithms
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying"). (Wikipedia).
A new map of the human brain could be the most accurate yet, as it combines all sorts of different kinds of data. This might finally solve a century of disagreements over the shapes and positions of different brain areas. Read more from Nature news here: http://www.nature.com/news/human-b
From playlist Neuro
11.1: Introduction to Neuroevolution - The Nature of Code
Welcome to a new topic in the Nature of Code series: Neuroevolution! 🎥 Next Video: https://youtu.be/kCx2DElEpP8 🔗 Toy-Neural-Network-JS: https://github.com/CodingTrain/Toy-Neural-Network-JS 🔗 Nature of Code: http://natureofcode.com/ 🎥 My Neural Networks series: https://www.youtube.com/p
From playlist 11: Neuroevolution - The Nature of Code
This presentation for medical professionals deals with the management of thyroid nodules. Solid lesions of the thyroid are very common. Ultrasound studies have shown that up to 70% of all human will develop thyroid nodules. The aim of the workup of these nodules to to identify those patien
From playlist Continue medical education
An general explanation of the underactive thyroid.
From playlist For Patients
Proof of the Convolution Theorem
Proof of the Convolution Theorem, The Laplace Transform of a convolution is the product of the Laplace Transforms, changing order of the double integral, proving the convolution theorem, www.blackpenredpen.com
From playlist Convolution & Laplace Transform (Nagle Sect7.7)
11.2: Neuroevolution: Crossover and Mutation - The Nature of Code
In this video I begin the process of coding a neuroevolution simulation and copy() and mutate() methods to the neural network library 🎥 Previous Video: https://youtu.be/lu5ul7z4icQ 🔗 Toy-Neural-Network-JS: https://github.com/CodingTrain/Toy-Neural-Network-JS 🔗 Nature of Code: http://natu
From playlist 11: Neuroevolution - The Nature of Code
I made an A.I. to play Atari Breakout better than you
I've continued to study artificial intelligence, where in this video we take on one of my favorite games - Breakout. I've coded up a genetic algorithm using a much more complex method than last time. Rather than using specific heuristics based on known strategies, I coded my own implementa
From playlist Artificial Intelligence & Machine Learning
Transcendental Functions 3 Examples using Properties of Logarithms.mov
Examples using the properties of logarithms.
From playlist Transcendental Functions
MarI/O - Machine Learning for Video Games
MarI/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World. Source Code: http://pastebin.com/ZZmSNaHX "NEAT" Paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Some relevant Wikipedia links: https://en.wikipedia.org/wiki/Neuroevolu
From playlist AI Research
Mapping The Brain | Digging Deeper
Should the United States spend billions to completely map the human brain? Will it ever be possible to build an artificial brain - and, if we do, what are the implications for the future? Join Ben and Matt as they talk about some interesting stuff that didn't make it into the Deceptive Bra
From playlist Stuff They Don't Want You To Know, New Episodes!
A3 More graphs and their functions
We expand to transcendental functions such a trigonometric functions. Ply around with the Desmos calculator software and learn more about the how variables that can appear in trigonometric functions affect the graphs of those functions.
From playlist Biomathematics
Live Stream #124.2 - Linting and Neuroevolution - Part 2
In part 2 of Friday's live stream, I begin discussing the topic "neuroevolution" which will be the subject of chapter 11 of the next edition of the Nature of Code book. (http://natureofcode.com/) 🎥 Live Stream Part 1: https://youtu.be/sIeN74GrYHE 22:54 - Neuroevolution Part 1 53:50 - Neu
From playlist Live Stream Archive
Live Stream #178: Neuroevolution Steering Vehicles
Try Dashlane here: https://dashlane.com/codingtrain. Get 10% off now with my promo code: "codingtrain". In this video, I tackle neuroevolution steering vehicles and discuss our new sponsor, Dashlane! 10:49 Rubik’s Cube (update) 19:55 Raycasting - Community Contributions 31:07 Neuroevol
From playlist Live Stream Archive
Coding Challenge #100.4: Neuroevolution Flappy Bird - Part 4
Welcome to part 4! In this section I attempt to improve the Neuroevolution Flappy Bird Coding Challenge. 💻Challenge: https://thecodingtrain.com/CodingChallenges/100.4-neuroevolution-flappy-bird.html 🎥 Part 1: https://youtu.be/c6y21FkaUqw 🎥 Part 2: https://youtu.be/YtRA6tqgJBc 🎥 Part 3: ht
From playlist 11: Neuroevolution - The Nature of Code
Expanding Logarithmic Expressions
How to use properties of logarithms to rewrite a single logarithmic expression as a sum/difference of logarithms. Hope this helps. Facebook: https://www.facebook.com/braingainzofficial Instagram: https://www.instagram.com/braingainzofficial Thanks for watching! Comment below with any
From playlist Precalculus
Coding Challenge #100.3: Neuroevolution Flappy Bird - Part 3
Coding Challenge #100 Part 3 In this video refine the neural network and genetic algorithm parameters as well as speed of the simulation during the training process. 💻Challenge: https://thecodingtrain.com/CodingChallenges/100.3-neuroevolution-flappy-bird.html 🎥 Part 1: https://youtu.be/c6
From playlist 11: Neuroevolution - The Nature of Code
Coding Challenge #100.5: Neuroevolution Flappy Bird - Part 5
Hold on there, last part! Here I add a feature for saving and loading a "bird brain" model. 💻Challenge: https://thecodingtrain.com/CodingChallenges/100.3-neuroevolution-flappy-bird.html 🎥 Part 1: https://youtu.be/c6y21FkaUqw 🎥 Part 2: https://youtu.be/YtRA6tqgJBc 🎥 Part 3: https://youtu.b
From playlist 11: Neuroevolution - The Nature of Code
Coding Challenge #100.1: Neuroevolution Flappy Bird - Part 1
Coding Challenge #100! In this challenge, I use the JavaScript neural network library and a genetic algorithm to train an agent to play Flappy Bird (see challenge #31). 💻Challenge: https://thecodingtrain.com/CodingChallenges/100.1-neuroevolution-flappy-bird.html 🎥 Part 2: https://youtu.be
From playlist 11: Neuroevolution - The Nature of Code
Transcendental Functions 18 More Examples 1.mov
More example problems.
From playlist Transcendental Functions