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Thanks a lot . The study of the subject was presented in an interesting and useful way. The source code of the GUI is missing. Can you add it to web

I want to have an offspring from you, where you will be a more fit parent

Why not passing the average weight of the parents to the offspring. Doesn't it improves the network diversity over when we copy the gene of only one parent?

Hello Finn, im trying to implements pacman with NEAT and i rlly struggle with it . if u can help me i will very appreciate it and please let me know

dose anyone know why in the last slide he killed 6 with fitness 442 but did not kill 5 and 7?

THANK YOU. This presentation was AWESOME, I understood it very well, thank you thank you thank you so much

love you, needed this soo much!

Everything is great except for the part about speciation. And it's not because of the animation! You're trying to be specific while also refering to future videos for the details you're being specific about. Don't repeat it five times. Just say that you categorize the genomes by cerain method that you'll show later and you'd skip a lot of confusion, and time!

Wow, great idea with inputs to the neural network. I was looking for an info on how to do it!

pronounciation +dangit... You said "gõringurschaaan" instead of "derivation". In instances such as this you might be causing someone to waste days because they heard you say something incorrect. Annuuuuunciate please

I'm not sure why you're using ArrayList<T> and HashSet<T> at the same time. IMHO single HashSet<T> will work just fine

There’s no problem with sigmoid , all activation functions have their uses

Sorry, I'm having a little trouble in understanding how to ensure that the nodes have consistent ids. Should the function that creates them take as arguments the ids of the input and output nodes of the connection it is splitting? What about more complex structures with many hidden nodes that interact? But generally though, great video. Definitely earned a like and subscribe!

GREAT VIDEO ,Thank you !

Great overview, thanks for this

can you provide the whole code for this project... i am interested in rewriting it in C++

Thank you very much for this introduction, It was very helpful

Hi, ich schreibe meine Facharbeit über den Lernprozess künstlicher Intelligenzen mit NEAT. bei 12:13 erklärst du mutate_node, in den offiziellen NEAT docs finde ich jedoch nur mutate_add_node und mutate_delete_node. Gab es dahingehend Updates? Desweiteren finde ich gar keine Information in den docs zu den anderen Mutation, wie mutate_weight_shift. Sind diese möglicherweise geupdadet wurden im Namen oder wurden komplett ersetzt? Vielen Dank im voraus

Thanks a lot!

Finn, thanks for great video series about networks. Love your engine!

I found a problem that if you set initial weights random from 0 to 1, the network breaks. Because the sum output: weights * prevNeuron is so high that the output_derivative is approximately 0. Hence, delta will be 0 and weights and biases will not be updated.

Hello. What resources did you use to learn NEA?. Have you only read the original paper or are there other great source to learn from?

Hello finn, i will strat learning neural network from this séries of vidéo, i know little about them, but i am good Java programmer and i had good background about ia, i worked before with aco ant colony algorithms.

thanks man

Ooooh only one representative client instead of comparing to every member of the species. Brilliant idea, thanks.

How can I do the following : Please help asap *1.* Make an Artificial Neural Network with dynamic input and binary out .... *2.* Make a Self Organizing Maps with dynamic input and binary out .... Use only C++ or Java

Hey, nice presentation! Could you please provide a link for formula that calculates genome distances, to sort genomes into species? Thanks!

I love how voice and its volume always changes

well, that's neat

Hey, great series! Love it. One thing though, I think you got the batch training concept wrong. Batch training is done so the weights are not updated AS WE TRAIN EACH INPUT over the batch. We accumulate the delta weights, and update them all at the end of the batch. The reason we do this, is because our final AIM is to minimize cost (error) over all the dataset (which is hard, hence batches). If this is confusing, I'm not sure how to explain this, but you can check 3Blue1Brown's 4 video playlist on Neural networks. He explains this in 3rd or 4th vid I think. Good luck all

Sorry for asking so much questions but this are the last ones... 1. How the selection of the species is made? how you score the results to then mutate that selection? 2. The nodes(neurons) what data contains?. Does it contains the weighted sum? like a normal neuron do? 3. How many types of NEAT algorithms are? 4. I read on the paper that there are some formulas you didn’t mention, what are those? like the fit formula, why those formula are useful?

I have a question. In what does the Encoding helps us, the Neat Encoding Scheme helps us only to visualize as a Genetically form? or is there another use? can you explain it to me?

I noticed you have a lot of getters and setters. I would suggest looking at a project Lombok. You can generate getters and setters by using annotations. You can even generate constructors, that initialize final values by using @AllArgsConstructor annotation before the class or generate data classes. There are more things it can do but that's the basics :)

The presentation was good, but I believe your explanation suffered from the lack of video editing. Some cuts could have made it all clearer and shorter.

Awesome work. After evolve, purging old links nodes not used by any gnome improves performance quite a lot

I got it working, the missing files are not needed. I also implemented a thread pool so rateClient can be parallelized. As for the game it self, I changed the inputs to 8 [0-6] are the number of free spaces in a direction (n, nw, w, sw, s, se, e, ne) plus [7] which is still the direction towards the food. A bit better results, than with just 4 inputs, and slower to evolve time. My best results I got was 109, starting with a gnome where all outputs are connected to all inputs and only weights and shift waits were allowed. Again, thank you for taking the time to post this.

best explanation on the internet. i'm doing an ecosystem project right now and tried to look up and understand NEAT algorithms but they were all far too technical for me, but the way you explained it gave me hope that i can implement this step by step. thank you. right now i have a simple feedforward network with no hidden layers for each of the creatures (just input/output and the directions they move) i suppose the next step would be to make it so the network prototype has some type of static method to generate a new hidden neuron/connection. this is going to be tough. even still this type of genetic algorithm fused with NN topic interests me way more than gradient descent/backpropagation calculus, so i think this path will be worth it for me in the long run as i just find this topic so much more interesting it would be great if this algorithm could be optimized even further somehow

It looks like ion your while loop while walking the connections, after the if (in1 == in2) and before if(in1 > in2) your are missing an else. Otherwise when in1 == in2 index_g1 will be incremented twice. Once for the equality and once for not being greater. since booths if will be executed in each loop. Haven't finish watching the rest of the series, so the suspense its building up. Anyway great series.

I love the work, I definitely want to watch through the series, I am especially confused about the "calculating" part because the random mutations cause cycles in my graphs. But why do you have jordan peterson in your NEAT playlist? I don't have a thing against the guy but it looks out of place.

Thank you!

very good explanation

I used NEAT to evolve my COC

I suck at chess, and checkers

Guys got nothing on me

Losers

Thanks, easy to follow👏🏾👏🏾

Hi, I do not know if you are still alive, but judging by your SOF, still how =) Why did you decide not to use your NEAT implementation?

this is amazing. you really are good at teaching these things. would love to see similar series on reinforcement learning methods like actor-critic or deep q-learning.

thanks

Why do you assigning zero to replacement index just right after getting a replacement index from neat? it's always zero in that case (0:40)