Nice video my friend. I would stay around for the seconds parts guys, exciting things ahead ;)

I could be insane, but I would swear that a rooster just taught me AI.

The talking chicken is *slightly* cursed. It’s probably because only the beak is moving and it’s opening a bit too wide for my taste.

He’s alive!

I'm finally starting to recover from like 3 years of intense burnout as a software engineer, and watching your videos helps me remember why I got into this field in the first place. Looking forward to the next one!

Since the controller can output an arbitrary cart speed every timestep, it is permitted more or less infinite acceleration. It would be interesting to see how they perform if the available acceleration, (or power, or whatever) is constrained to realistic bounds

Now build entire ecosystem with Darwin's evolution principles.

From a frenchmen to another, your accent has improved so much! Your videos are captivating as always.

Can't wait to see your next video 🙌🏾🙌🏾

Love your work Pezzza. You are a great inspiration.

Finally ! I just love your content, i'm happy to see you again, also on my favorite topic

I was literally about to work on a project of mine that requires AI, immaculate timing pezzza

In the next videos I suggest including some articles, in the description, to delve deeper into the topics discussed. As I was searching for the sources for this project, I couldn't find them.

Love your videos and can't wait for the next one!

I’ll binge-watch this rn

Extremely excited for the next video! For some reason, AI training videos just scratch a certain itch so nicely

I love how you animated this! It looks so cool and made it easy to follow along

This video is super nice man, waitin for the second part!

The first steps are always the hardest, so it might be good to show some actual code examples for them.

I love this channel. It's enjoyable to just watch cool coding stuff.

Really inspiring. Will be waiting to see the code! Great content :)

Great video! Glad to see you again

I did not expect you to say "double pendulum" and now you are leaving us on a cliff hanger like this :( Hope next episode appears soon

I love your videos ❤ ai is such an interesting topic and I'll watch part2 as soon as possible

The guy is back :D

Your projects are amazing, and often very beautiful. I aspire to make software as good as this one day!

Great video! Hyped for the double pendulum :)

This is amazing! I can actually understand how to create my own system if I wanted. Unlike some videos which don’t succeed at telling us in an understandable way.

Thank you SOOOO MUCH!!!! I always wanted to do something like this, this will really help!!!

Great video! It's a very nice visual representation ... that must have been a ton of work!

I'll admit I wasn't as interested in this one as your other work, but your animation quality is *chef's kiss* such a good presentation

Great video! as always!

Great video as usual :D

Thank you for a great video

Awsom video, cant wait for the second part

im trying to learn ML and DL by myself and find this super helpful, waiting for your next masterpiece

Double pendulum should be fun. I wonder how good a solution it will be able to find. As an aside, can't you just use the raw pendulum height (summed per frame) as a fitness function?

inverted double pendulum sounds intense! looking forward to it. :)

That is fun! Good work

Loved the UIs

I was wanting a video on machine learning!! It's from you aswell!

Waiting for the next one!

This will actually help me so much in my new project

such a crime that one of the best youtubers in the tech space only has 138k views and only 34k views on this video after 7 days

YES :D

I can't wait to see what happens next with the double pendulum !

soooo, now I want to watch the next video, it is so excited

I do topological sorting, but then I cache all the pathways from input nodes to output nodes into lists. This way, instead of re-iterating the graph, I just do a for loop to iterate over lists of nodes instead of repeatedly recalculating paths. I haven't seen this technique used online but it makes training step much faster.

Very neat video

Great work! :-)

That trippy music!

Wonderful!

Very neat (haha)! Also great classical controls problem!

nice! it reminds me of the MarI/O video, it uses this algorithm too

Waiting for the next video!

im excited for the next video

Very cool!

So cool example! Can't wait to see how AI will handle chaotic pendulum ;)

Hes back lets go!

The talking chicken is amazing

Yay he's back

This was again an awesome and beautifully visualised video, just like I'm used from this channel. I'd love to be able to program something like this. If you were to make a more detailed tutorial that we can follow along with the videos and description I believe many people might benefit from this. Is your code open source? Thanks a lot for your awesome content!

So after all these months in hiding, you've been secretly building Boston Dynamics Spot knock-offs 😂

amazing work and i hope mine will work

yes he uploaded

I'm still curious about the UIs :) anxiously waiting for the source code ❤

This video seems much simpler than your others. Although I hope a bit more math and code examples can be used in the next. Gate logic videos seem great to teach the subject aswell. Although I feel such simplicity makes it hard to understand the topic clearly enough to extend it to more complicated matters.

This is an absolutely wonderful video. If I may provide some feedback; It would be great if you could visually show how these mutations (5:47) are applied using the network chart. When splitting an existing connection in two, do they share the same connections with their parents and children? Do new connections have random weights? Also, why does my hyperbolic tangent function provide values different from yours?

cant wait for part 2 🙂

Really cool video, thank you! This is an really interesting sub topic of ML, especially with such simple networks. Im wondering how it would play out, to create some intermediate game ai, which handles some decisions with this sort of mechanisms instead of a huge load of switch/if/else shenanigans. Obviously not driven fully by it for performance reasons, but in an assisting way. Btw, can you add the used resources (wiki/paper links) to the description? I would appreciate it. Thank you!

Very well explained! I wonder how the network would react to slight random fluctuations in the value of the nodes or just straight up removing nodes (How would it adapt?)

Yeah.. the talking cockerel with sunglasses talking about NN is weirding me out.. Great video!

What a hook!

Yay!!!

I really like the NEAT-algorithm and its variations. I've used them to find shapes for the unsolved mathematical problem called moving sofa. I got something similar to the currently known best solution in just a few dozen iterations. I wonder how this algorithm scales with more complex task on just a few inputs.

I wanted to do the same (create my own evolution neural network) for a long time and you gave me the motivation to ! Thank a lot Your video is really great ! Love it ! I have one question, on my side I have one issue. Network are get over complexed really fast, creating many neurons in fact not necessary. How did you managed this ? Do you decrease the chance to create a new neuron depending on the current number ? Do you take the size of the network in account when scoring ? Or maybe you didn't have this issue ! If anyone have an idea about it, I take it ! (I resolved the issue by decrasing the score depending on the network size, but I'm not feeling this is a good way to solve it)

Hurry up with the second part, I can't wait!

awesome video man, the graphics are super beautiful as always. you inspired me to make a network of my own, what sources did you use to learn the intricacies of the architecture? i understand the general flow but wouldn't know when at what rate should i add connections or nodes. thanks 😄

Was nice to see a none layered net ! But just a DAG net.

G ❤️🔥

Talking chicken? I'm in!

That's an awesome video. Crazy good graphics! Mind giving us a hint on how you made them?

48 seconds ago!

im exited to see it with a double pendulum

after this video you convinced me to play around with neutral networks. i quickly found my first goal - make MNIST number recognition network. its my second day trying and the network consistently gets 30% error rate which really pisses me off, this must be because i did not use any libraries and slapped it together on a Unity C# project with a help of chat gpt. im planning to run network on a separate thread so i could test more variety of settings for the neural network. anyways if you are reading this, what would you suggest for me? im using traditional neural network with inputs of 784 hidden layer 128 hidden layer 32 output layer 10 and learning rate of 0,0005 in each epoch it eats 60k images after running first half of first learning epoch it shows 25-30% error rate, after second epoch error rate tends to move closer to 50% which is weird, i tried making learning rate smaller but that requires lots of time too

I feel like I've learned more in this video than in 5 hours of reading. Will you also talk about other learning algorithms?

In theory I understand training AI, however what software do you use? Where do you write logic? How do you get an image output? How do you offload the work to a GPU for faster matrix processing? Interesting stuff, nonetheless. Great video!

Brad Pitt: “ya like DAGs?”

babe wake up, we got to watch this now

Cool video! Btw, do you use Godot or some other engine? If though, how to calculate physical interactions in seconds? Because usually it is 1 scene with fixed speed of time, and to calculate all those ai decisions everything should be sped up and without graphics

So few nodes! Unity's ML Agents default configuration is 2 layers of 128 nodes, I dont understand why it uses so much, maybe something related to reinforced learning?

Double pendulum 😨

+1 for part 2

The pendulum visualization looks really nice and tactile. Is it custom made, or was there some library/framework involved?

The random disturbances are interesting. It's a simple version of the Boston Dynamics quadruped that they kick to try and knock over and it recovers quickly. You can tell that they are using a similar algorithm to find the result, or perhaps it's the fitness function? Make you realise how they haven't gotten much further, intelligence wise from this demo. Robotics hasn't had its "Image Net" moment yet. Splendid video, love the visual description of the algorithm! Keep 'em coming man!

Super insightful! What tool are you using for animations?

Nice work! Is it possible that you publish this project would very interesting to read the code.

Congratulations for the video! Could you please tell me which software you use to build the animations ?

Encore une super vidéo. Tu l'as codé dans quel langage ce projet? C'est quoi le cpu de la machine sur laquelle tu fais tourner le processus de sélection?

Can u give an example recommendation for a mobile app for this?