Please share this video if you know somebody whom it might help. Thanks :) edit: Some people correctly identified the 3Blue1Brown style of the video. That is because I am using the python library manim (created by 3Blue1Brown) for the animations. Link and more information in the description. Huge thanks for all the likes and comments so far. You guys are awesome!
I can't say anything that hasn't been said already: This video is golden. The visualization, explaining, everything is just so well done. Phenomenal work. I'm basically commenting just for the algo bump this video rightfully deserves.
As an ML beginner, I know how much effort and time is needed for such visualization of a program. I would like to give you a huge thumb! Thank you for the video.
FANTASTIC video. Doing Stanford's Coursera Deep Learning Specialization and they should be using your video to teach week 4. Much clearer and far better visualized. Clearly, you put great effort into this. And kudos to using 3Blue1Brown's manim lib. Excellent idea. I am going to put your video link in the course chat room.
You've just explained very clearly in a single video what others try to vaguely explain in series of dozens videos. Thank you. Fantastic job! Looking forward to more great videos from you.
I know I'm watching this 2 years after it was released but I really can't stress enough how helpful this is. I've seen heaps of videos explaining the math and heaps of videos explaining the code but this video really helped me to link the two together and demystify what is actually happening in both.
This is so interesting. I always wondered how a neural net works... but it's also good to remind ourselves that we're not as clever as we thought. I feel humbled knowing that there's some fierce minds out there working on these complicated problems.
This is the kind of video that I was looking for to get beyond the basics of ML and start gaining a better and deeper understanding. Thank you for putting the effort into making this great video.
yes but wasnt the value before sigmoid in the last cell 9 ? precisely I got something like 8.998 If I missed something please explain I want to know why is that
It's great to see content that helps demystify complex topics like neural networks, especially using a versatile language like Python! Understanding neural networks is so vital in today's tech-driven world, and Python is a fantastic tool for hands-on learning. It's amazing how such concepts, once considered highly specialized, are now accessible to a wider audience. This kind of knowledge-sharing really empowers more people to dive into the fascinating world of AI and machine learning! 🌟🐍💻
Such a great video -- high quality and easy to follow. Thanks. One typo in Additional Notes; (X,) + (1,) == (X, 1) -- this is shown correctly in the video, but in the Notes you show result as (1, X)
Welcome back dude! I have been waiting your comeback for so long Please dont go away this long next time :) Great editing and audio quality btw Reminds me of 3b1b
Nice job with the explanation!!! I felt like I was watching a 3blue1brown video! A few notes: 1.)You should run through examples more often because that is one of the best ways to understand a concept. For example. you should have run through the algorithm for the cost function so people understand it intuitively. 2.)It would be nice if you went more in depth behind backpropagation and why it works. Things you did well: 1.)Nice job with the animations and how you simplified them for learning purposes, the diagrams would be much harder to understand if there was actually 784 input layers. 2.)I love the way you dissect the code line by line! I cant wait to see more videos by you I think this channel could get really big!
Thanks for a very clear explanation. I was doing the same from scratch in python, but got stuck at dimensioning the weight matrices correctly, especially in this case with the 784 neuron input. Now i can check if this helps me to complete my own three layer implementation. 😅
Excellent video and accompanying code. I just keep staring at the code, its art. And the naming convention with the legend is insightful, the comments tell the story like a first class narrator. Thank you for sharing this.
Well done, i couldnt ask for a better video, Germans make the best and most detailed educational videos here on youtube. The pupils of the world say thank you.
Great Video! However, you say that "Mean Squared Error" is used as loss function and you also calculate it. However "o - l" (seemingly the derivative of the loss function) isn't the derivative of MSE. It's the derivative of Categorical Cross Entropy ( -np.sum(Y * np.log(output)), with Softmax before it). Anyways, keep up the great work :)
This video is pretty great, although i had to pause and sketch along and keep referring to the code you showed, it definitely helped me understand better how to do it
hey this was a great into! Gave a good playing ground to experiment with in increasing the nodes of the hidden layer, changing the activation function and even adding an addition hidden layer to evaluate the effects/effectiveness! With more epochs could get it above 99% accuracy (on the training set, so might be overfitted, but hey_)
Thank you for this amazing video. I understand the huge effort in the animations and I am so grateful. I believe this is THE video for anyone trying to get into machine learning.
The first minute of this video got myself asking who is this dude and does he make more videos explaining compicated topics in a simple way. pls do more
Well , your brain is basically a complex neural network Plus, our body isn't us; our brain is us. We are just a complex meat neural network controlling a big fleshy, meaty and boney body.
Such an amazing video with lots of visualization. But I don't think you can simplify delta_o to "o - l" with whatever mathematical tricks. It needs to be "(o - l) * (o * (1 - o))".
Source text Excellent video. Best of the best ) i want to see more and slowly about backpropagation algorithm. It is most interesting moments.. maybe better to make the step by step slides?
For running the code on Ubuntu 20.04 you need to do this: git clone github.com/Bot-Academy/NeuralNetworkFromScratch.git cd NeuralNetworkFromScratch sudo apt-get install python3 python-is-python3 python3-tk -y pip install --user poetry ~/.local/bin/poetry install ~/.local/bin/poetry run python nn.py
at 14:32 you use the updated weights (to the output layer) to calculate the hidden layer deltas. I never saw anyone doing it this way. Usually, the old weights are used and all weights are updated after backprop. I don't think it makes a large difference but I wonder if this is intentional or I am missing something.
Thank you so much for posting this comment, i was wondering why my model was losing accuracy with every epoch. With that little change my accuracy jumped from 20'000 / 60'000 to 56'000 / 60'000
@@BotAcademyYT Nooo, we cant wait.... i shared this video across all discord servers, and most of em asked , wish this guy could make a video like this on NEAT or hyperNEAT. because there isnt much resources out there. Hope you will make it!
I really loved your video, it's so clearly explained. I have a kind of big question. What if you had a data frame where all the columns are related to each other, but there are different functions for certain parts of it? I'm trying to make a neutral network that is meant to understand the functional parts of proteins, in order to create new proteins
Thanks! That's a really hard one :D If there is some temporal difference in the data, you'd need a recurrent NN like an LSTM. But I think its not the case for proteins. So if they are related to each other I guess you'd flatten the data frame and use it as input. If the input dimension is too large, I think you need some other feature extraction technique before applying a NN. But I am just guessing here tbh. There might be better approaches directly for proeteins (there are surely some good papers out there because its a topic with quite some research behind it)
Sir! can u please explain how to design and write a code for a SNN for digit recognition using unsupervised lifelong learning algorithm in pycharm. I hope you will explain soon, I am exited for this video.
for the first node in the hidden layer you added the bias node of 1, for the rest of the nodes in the hidden layer you multiplied the bias node of 1 ??
![Hopfield network: How are memories stored in neural networks? [Nobel Prize in Physics 2024] #SoME2](http://i.ytimg.com/vi/piF6D6CQxUw/mqdefault.jpg)
Please share this video if you know somebody whom it might help. Thanks :)
edit: Some people correctly identified the 3Blue1Brown style of the video. That is because I am using the python library manim (created by 3Blue1Brown) for the animations. Link and more information in the description. Huge thanks for all the likes and comments so far. You guys are awesome!
WOW FIRST TIME TO KNOW HOW 3Blue1Brown Work Awesoome
Can you plz help me with this .. I want a chess ai to teach me what it learnt
ua-cam.com/video/O_NglYqPu4c/v-deo.html
Can you share your video manim code?
Incredible video. Not because of your insight, but because how you use visuals to represent the information as digestible as possible.
I can't say anything that hasn't been said already: This video is golden. The visualization, explaining, everything is just so well done. Phenomenal work.
I'm basically commenting just for the algo bump this video rightfully deserves.
As an ML beginner, I know how much effort and time is needed for such visualization of a program. I would like to give you a huge thumb! Thank you for the video.
a thumbs up you mean?
His thumb is now massive
I dont think he want no thumbs
He will be in pain if you will give him such a huge thumb... just give him a thumbs up
FANTASTIC video. Doing Stanford's Coursera Deep Learning Specialization and they should be using your video to teach week 4. Much clearer and far better visualized. Clearly, you put great effort into this. And kudos to using 3Blue1Brown's manim lib. Excellent idea. I am going to put your video link in the course chat room.
You've just explained very clearly in a single video what others try to vaguely explain in series of dozens videos. Thank you. Fantastic job! Looking forward to more great videos from you.
I know I'm watching this 2 years after it was released but I really can't stress enough how helpful this is. I've seen heaps of videos explaining the math and heaps of videos explaining the code but this video really helped me to link the two together and demystify what is actually happening in both.
This is so interesting. I always wondered how a neural net works... but it's also good to remind ourselves that we're not as clever as we thought. I feel humbled knowing that there's some fierce minds out there working on these complicated problems.
That's it! Comprehensive training code loop for a 1 hidden layer NN in just 20 lines. Thank you sir!
As someone beginning machine learning, this video was so useful to really getting a deep understanding on how neural networks work!
This is the kind of video that I was looking for to get beyond the basics of ML and start gaining a better and deeper understanding. Thank you for putting the effort into making this great video.
for those actually implementing something, note at 12:08 the values in the hidden layer change back to how they were pre sigmoid application
good point! Must have missed it when creating the video.
yes but wasnt the value before sigmoid in the last cell 9 ? precisely I got something like 8.998
If I missed something please explain I want to know why is that
It's great to see content that helps demystify complex topics like neural networks, especially using a versatile language like Python! Understanding neural networks is so vital in today's tech-driven world, and Python is a fantastic tool for hands-on learning. It's amazing how such concepts, once considered highly specialized, are now accessible to a wider audience. This kind of knowledge-sharing really empowers more people to dive into the fascinating world of AI and machine learning! 🌟🐍💻
Such a great video -- high quality and easy to follow. Thanks.
One typo in Additional Notes; (X,) + (1,) == (X, 1) -- this is shown correctly in the video, but in the Notes you show result as (1, X)
Thank you very much for pointing out the inconsistency. You're right, it is wrong in the description. I just corrected it.
This is a great video even for those who are not into this field. Great voice and explanation of how neural networks work.
Welcome back dude!
I have been waiting your comeback for so long
Please dont go away this long next time :)
Great editing and audio quality btw
Reminds me of 3b1b
Thanks! I'll try uploading more consistently now that I've finished my Thesis :)
"does some magic." Great explanation. Thanks.
Listen to the man, listen well. He is giving accurate and incredibly valuable knowledge and information that took me years to learn.
I can tell you put a lot of work into this. You deserve more views! (also commenting for algorithm)
Nice job with the explanation!!! I felt like I was watching a 3blue1brown video! A few notes:
1.)You should run through examples more often because that is one of the best ways to understand a concept. For example. you should have run through the algorithm for the cost function so people understand it intuitively.
2.)It would be nice if you went more in depth behind backpropagation and why it works.
Things you did well:
1.)Nice job with the animations and how you simplified them for learning purposes, the diagrams would be much harder to understand if there was actually 784 input layers.
2.)I love the way you dissect the code line by line!
I cant wait to see more videos by you I think this channel could get really big!
Thank you very much for the great feedback!
Saw your post on Reddit. Thank you very much for the work you put in your videos. New subscriber.
Thanks for a very clear explanation. I was doing the same from scratch in python, but got stuck at dimensioning the weight matrices correctly, especially in this case with the 784 neuron input. Now i can check if this helps me to complete my own three layer implementation. 😅
oh hell yeah :) just got yourself a new subscriber, support your small channels folks
Excellent video and accompanying code. I just keep staring at the code, its art. And the naming convention with the legend is insightful, the comments tell the story like a first class narrator. Thank you for sharing this.
Well done, i couldnt ask for a better video, Germans make the best and most detailed educational videos here on youtube. The pupils of the world say thank you.
Amazing video. Especially the matrix effect on the code in the first second. Love it.
Great Video! However, you say that "Mean Squared Error" is used as loss function and you also calculate it. However "o - l" (seemingly the derivative of the loss function) isn't the derivative of MSE. It's the derivative of Categorical Cross Entropy ( -np.sum(Y * np.log(output)), with Softmax before it). Anyways, keep up the great work :)
Amazing video! The explanation was very clear and I understood everything. Really hope you're gonna be posting more videos on neural networks.
This video is pretty great, although i had to pause and sketch along and keep referring to the code you showed, it definitely helped me understand better how to do it
awesome video :) i did not know that you could use that trick for the mse 👍
Wow! Thanks you so much. You rock. Now looking forward to "Transformers Explained from Scratch using Python" ;)
Subscribed. We need more channels like this!
hey this was a great into! Gave a good playing ground to experiment with in increasing the nodes of the hidden layer, changing the activation function and even adding an addition hidden layer to evaluate the effects/effectiveness! With more epochs could get it above 99% accuracy (on the training set, so might be overfitted, but hey_)
Very well explained. I really liked it. making noise for you. Please make such video to understand NLP in the same intuitive way. Thank you :)
Best video to this kind of topic on the whole market. Very well done! 😀
The BEST video ever! Hats off to your efforts and a Big Big Thanks for imparting the knowledge to us. I will never forget the concept and ever. 😊
yes , finally I had found the golden channel
thanks budd
This is really helpful for beginners!! Great work always appreciated bro!!
Love this so much. So organised and was really helpful. So glad you put the effort into the animation. Keep up the great work!
Superbly illustrated! Thanks for sharing.
This video feels like a 3Brown1Blue video, and I like it.
Best video of ML so far i have seen.
Fantastic job with your explanation and and especially the animations. All of this really helped to connect the dots
Thank you for this amazing video. I understand the huge effort in the animations and I am so grateful. I believe this is THE video for anyone trying to get into machine learning.
Amazing visualized explanation
The first minute of this video got myself asking who is this dude and does he make more videos explaining compicated topics in a simple way. pls do more
Such an awesome video! Helped me a lot to understand neural networks. Thanks a bunch!
This is one of the best explained videos i've seen for this. great job!
Hope this comment helps :)
Making noise for you, good luck!
Great explanation - Thank you for making this!
Excellent content and illustration 🎉
As a neural network, I can confirm that we work like this
Well , your brain is basically a complex neural network
Plus, our body isn't us; our brain is us. We are just a complex meat neural network controlling a big fleshy, meaty and boney body.
Such an amazing video with lots of visualization. But I don't think you can simplify delta_o to "o - l" with whatever mathematical tricks. It needs to be "(o - l) * (o * (1 - o))".
I think you're right, but for some reason it seems to work anyway
yess i have been trying to understand this for weeks 🤯
12:40 why dont we use derivative of activation function for delta_o? But we used derivative of activation function for delta_h. Any answers???
I was confused about the same thing, did you figure it out?
Great explanation and hats off to ur efforts for these visualisation!!! 🎉❤
Source text
Excellent video. Best of the best ) i want to see more and slowly about backpropagation algorithm. It is most interesting moments.. maybe better to make the step by step slides?
This video is perfect for beginners, thank u so much
For running the code on Ubuntu 20.04 you need to do this:
git clone github.com/Bot-Academy/NeuralNetworkFromScratch.git
cd NeuralNetworkFromScratch
sudo apt-get install python3 python-is-python3 python3-tk -y
pip install --user poetry
~/.local/bin/poetry install
~/.local/bin/poetry run python nn.py
Video Level: Overpowered!
at 14:32 you use the updated weights (to the output layer) to calculate the hidden layer deltas. I never saw anyone doing it this way. Usually, the old weights are used and all weights are updated after backprop. I don't think it makes a large difference but I wonder if this is intentional or I am missing something.
I also think this is a mistake. Andrew Ng emphasized that the weights must be updated after calculating the derivatives.
@@FlyingUnosauryou are talking about the derivative of activation function right?
Thank you so much for posting this comment, i was wondering why my model was losing accuracy with every epoch. With that little change my accuracy jumped from 20'000 / 60'000 to 56'000 / 60'000
How would you do the 50000 samples for training? Great video by the way!
Glad to have you back :D
Gets even more tricky at 50 metres one handed - weak hand!
intuitive video on the whole planet, likewise can you come up with a brief explanation on NEAT algorithm as well ?
Thanks! I‘ll add it to my list. If more people request it or if I‘m out of video ideas, I‘ll do it :-)
@@BotAcademyYT Nooo, we cant wait.... i shared this video across all discord servers, and most of em asked , wish this guy could make a video like this on NEAT or hyperNEAT. because there isnt much resources out there. Hope you will make it!
Amazing video I hope this gets more views.
This was actually very good! Subscribed.
You create some amazing content. Really well explained.
Thanks. I wonder if I could train it for other pictures?
Thank you for this. Fantastic video.
I really loved your video, it's so clearly explained. I have a kind of big question. What if you had a data frame where all the columns are related to each other, but there are different functions for certain parts of it?
I'm trying to make a neutral network that is meant to understand the functional parts of proteins, in order to create new proteins
Thanks! That's a really hard one :D If there is some temporal difference in the data, you'd need a recurrent NN like an LSTM. But I think its not the case for proteins. So if they are related to each other I guess you'd flatten the data frame and use it as input. If the input dimension is too large, I think you need some other feature extraction technique before applying a NN. But I am just guessing here tbh. There might be better approaches directly for proeteins (there are surely some good papers out there because its a topic with quite some research behind it)
awesome man. Could you please tell me how do you realise this neural animation? It's really nice
Algy comment right here, thanks for the wonderful video!
Great content, subscribed!
Amazing video really helpful!
Excellent video
Great work! thanks for the explication
Great video
you got a subscriber ❤
Now, do it again but IN Scratch!😊
Really nice video, keep doing those!!
11:31 What if the first output neuron wasn't the one with the highest value? Does that mean that the neural network classified the image incorrectly?
Exactly :)
Finely, someone who explained this to me so I understand
Sir! can u please explain how to design and write a code for a SNN for digit recognition using unsupervised lifelong learning algorithm in pycharm. I hope you will explain soon, I am exited for this video.
extremely thankful
Great video, really interesting!
you are the man! My respect goes out to you
Thanks a lot sir
Wow amazing
I have a data set with the same size, how do I change the data set? I have tried to change it but failed. BTW thank you this video really helped me
Fantastic video. I really appreciate the effort u_tubers put into great videos like this one.
its majestic
brilliant
Great explication. Plz what's the software used to make this video ?
Thanks! github.com/ManimCommunity/manim
how could i split the data into training and testing data?
thank you by your excellent video
Lucid explanation !!
for the first node in the hidden layer you added the bias node of 1, for the rest of the nodes in the hidden layer you multiplied the bias node of 1 ??
bro how did u make the first layer know how much number of color scale should be used on the img
Excellent
Good one
great video, thanks!