Most TH-camrs use the minimax algorithm, but using neural networks for chess is truly special. You are a legend! I would love to see a video on how we can contribute to an open-source engine like Stockfish.
Hi Nick! I have no idea about this, but I have a doubt... Is your training model restricted by your RAM memory? Can't it be done in many stages? Is it possible to Plug in two computers in series and get the computation quicker? BTW sweet video! Watched till the end. Keep it coming 🎉❤
Hey! Thanks for your support, I appreciate it! ❤ About RAM - yeah, it's a common problem with neural networks, esp. CNNs, because of the size of tensors X and Y. But you can manage it in stages. Just remember, the last layer's size has to match the vocabulary (total number of legal moves in the dataset). This layer must stay fixed-you can't change the NN structure after training. So, start by creating a big array of unique targets (as long as it fits in memory). Once you've fixed the size and set up mappings like int_to_move & move_to_int, you can split X into segments (X_1, X_2, ..., X_n) and train the model iteratively with each segment. It should work fine, but it'll take hours, days, weeks, months... Using multiple computers: Yep, it's possible (called "distributed and parallel training"). TensorFlow and Keras support this approach, though I haven't tried it myself yet (
I was hoping you'd show how to build a neural net from scratch. That is, instead of using a pre-made library, start with nothing beyond numpy and pandas, and go from there. My problem with python is that other people have already done most of the work, so all a person really needs to do is download someone else's pre-made set of solutions and learn how to use that solution's commands. I guess what I'd really like to see is how to build a neural net in Assembly or something 🙂
Hey, "TensorFlow" is in the title and on the thumbnail 😁. That's a great idea, thank you! I want to do it, though not in assembly, I'm afraid. But in C/C++ with the usage of GSL for linear algebra, I guess
In ML and DL, people usually use Python, unless it's for HFT. Speed was not my primary concern in this project, so coding with Python was much faster and easier. That's it. Typically, drafts are written in Python first and only rewritten in C/C++ if necessary
![fellow fellow - PROUD feat. INK WARUNTORN [STRING QUARTET]](http://i.ytimg.com/vi/BQrVkMPTdHE/mqdefault.jpg)
![[TH] VALORANT Champions Seoul - Lower Bracket Final - LEV vs TH](http://i.ytimg.com/vi/fyYkI80GG4o/mqdefault.jpg)
second part (huge improvement): th-cam.com/video/-WDRaRUjRMg/w-d-xo.html
Most TH-camrs use the minimax algorithm, but using neural networks for chess is truly special. You are a legend!
I would love to see a video on how we can contribute to an open-source engine like Stockfish.
Stockfish can already easily defeat any human, as far as I know 😄 And then there's AlphaZero as well... 😱
Still, it's a nice idea, thank you!
Im gonna come back after I learn python, this is like alchemy to me rn
Best of luck to you in learning Python! When you come back, feel free to ask any questions!
Good one mate
Thanks! I appreciate your kind comment!
Yeahhhh, new video finally!👍
Thnx a lot!
Thanks for watching! More content is on the way, so stay tuned! 😊
Make this video go viral
really cool project!!
Thanks man!!
Glad you like it! Thanks for kind words!
nice vid!
Hi Nick!
I have no idea about this, but I have a doubt... Is your training model restricted by your RAM memory? Can't it be done in many stages? Is it possible to Plug in two computers in series and get the computation quicker?
BTW sweet video! Watched till the end. Keep it coming 🎉❤
Hey! Thanks for your support, I appreciate it! ❤
About RAM - yeah, it's a common problem with neural networks, esp. CNNs, because of the size of tensors X and Y. But you can manage it in stages. Just remember, the last layer's size has to match the vocabulary (total number of legal moves in the dataset). This layer must stay fixed-you can't change the NN structure after training. So, start by creating a big array of unique targets (as long as it fits in memory). Once you've fixed the size and set up mappings like int_to_move & move_to_int, you can split X into segments (X_1, X_2, ..., X_n) and train the model iteratively with each segment. It should work fine, but it'll take hours, days, weeks, months...
Using multiple computers: Yep, it's possible (called "distributed and parallel training"). TensorFlow and Keras support this approach, though I haven't tried it myself yet (
@@_NickTech Cool! Thanks for the explanation. I would love to see this project turned into a series...
I was hoping you'd show how to build a neural net from scratch. That is, instead of using a pre-made library, start with nothing beyond numpy and pandas, and go from there. My problem with python is that other people have already done most of the work, so all a person really needs to do is download someone else's pre-made set of solutions and learn how to use that solution's commands. I guess what I'd really like to see is how to build a neural net in Assembly or something 🙂
Hey, "TensorFlow" is in the title and on the thumbnail 😁. That's a great idea, thank you! I want to do it, though not in assembly, I'm afraid. But in C/C++ with the usage of GSL for linear algebra, I guess
Why python, why not any other faster languages?
In ML and DL, people usually use Python, unless it's for HFT. Speed was not my primary concern in this project, so coding with Python was much faster and easier. That's it.
Typically, drafts are written in Python first and only rewritten in C/C++ if necessary