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.

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

He’s alive!

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 🙌🏾🙌🏾

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

Love your work Pezzza. You are a great inspiration.

I’ll binge-watch this rn

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.

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

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

the visualizations are epic

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

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

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

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

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

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

Great video! Glad to see you again

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

Now build entire ecosystem with Darwin's evolution principles.

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

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

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

Great video! as always!

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

The guy is back :D

Videos like these remind me why I got into this field. Man my job is sucking the soul out of me. Gotta do something about this.

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

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.

Great video! Hyped for the double pendulum :)

Very nice video, thanks for that

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

Awsom video, cant wait for the second part

This will actually help me so much in my new project

Loved the UIs

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?

That is fun! Good work

Great video as usual :D

Thank you for a great video

Wonderful!

Very neat video

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

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

Waiting for the next one!

Very interesting video! The editing and animations are so nice and makes it easy to understand, and I was wondering, what software did you use to make them?

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

Waiting for the next video!

Great work! :-)

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

What would be neat is to take an AI model that has been trained in realist physics and see if it can be applied to balance a real-world physical pendulum. There are forces and things in real-world physics that you cannot account for in a software simulation. For example, there are electrical forces, such as wire resistance, with the electric motor that drives the cart. The friction forces of the rail and the cartwheels, forces of wind if the balance operation occurred in an outside windy environment, and more. It would be fun to build something like this and train a model to control a real-world pendulum!!

im excited for the next video

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 cool!

That trippy music!

I always found these systems fascinating, adding noise made me wonder about one thing: what if there was noise on the neurons themselves? In the real world neurons live in a chaotic systems too, so it stands to reason that there'd be sources of noise there too.

Yay he's back

Hes back lets go!

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

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

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?

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

YES :D

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

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.

amazing work and i hope mine will work

What a hook!

cant wait for part 2 🙂

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

yes he uploaded

Nice one!

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

Yay!!!

The talking chicken is amazing

Talking chicken? I'm in!

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

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

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!

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

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

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?)

im exited to see it with a double pendulum

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!

excelent video. I learn a lot. Do you have any video where you explain the code?

That's amazing. I really like what you have done here. Can you share some of the code used in this video? It would be appreciated.

Hi Pezzza. I have got a question for a physics rigid body particle simulation you made ages ago, and I am dying to know since you didn’t upload the source code and can’t find what I need anywhere else. You have the feature where you are able to draw your own rigid bodies with the mouse. I believe the objects are composed of particles which are interconnected by constraints (correct me if I am wrong). I know how to implement everything apart from the constraint connections. How do I connect the particles in a way that works for any drawing and keeps the object's rigidity. A naive approach be to connect every particle to every other particle but that would suffer from performance issues. How did you implement it?

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

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.

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 😄

babe wake up, we got to watch this now

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)

G ❤️🔥

+1 for part 2

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

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

Super insightful! What tool are you using for animations?

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!