Finally, someone came up with this!! Really really grateful to you for this initiative. I hope you continue this playlist and teach every bit of Deep Learning from scratch.
These are all from a master's hand! Somehow writing them and having all your mind and body moving and connecting to that pen to move these ideas into images, charts and graphs is more cognitive than just really fancy computer generated graphics. It really goes into your mind!
This video very help me to learn fundamental theory of mathematics inside the Neural Network, hope u always healty. Thankyou so much Prof Raj Dandekar🙏🙏🙏🙏
Freakin 35 videos?, Uff I really need more hours in my day, And it's really nice to see from scratch ml explained in very detail like this, Definitely on my to-do list to try these out
Thank you for this amazing series. It really helps in in-depth understanding of the Neural Network along with coding. I am currently at lecture 5, and wanted to point out that Lectures need to be in order (ascending order) of Lecture numbers in the playlist. Secondly the superimposed camera should only capture your image, not extra things like room (Lecture slide/code get behind you). I hope these changes will enhance user experience.
Thanks for the feedback! All lecture names have been changed to reflect the order of lectures such as Lecture 1, Lecture 2 etc. The camera part has been implemented for all lectures after lecture 3.
Hi, thanks for this! please take this series towards its logical conclusion i-e towards the latest LLMs/ GenAI as I feel no one explains concept+code in a structured way from scratch and lots of gaps in understanding are left. Also, your picture frame is hiding the code part. Hope it is resolved in the next videos. Thanks again!
Hello Raj, just one small suggestion, the video-over-video overlay is kind of hiding the main content, can you please move this to any suitable location while explaining. please check between 23-26 mins what I am talking about as the captions are also overlapping the code. Thanks again for your efforts! waiting for more learning and hands-on on this series.
I'm a software developer with no background in ML. Really interested to see this series unfold. I'd love if you could move the face cam display a little more towards the left of the screen as it hides the content.
Is this series separate from "Teach by Doing ML"? I'm a little confused because the neural network basics video is also in that playlist. Is this video a continuation of your last video on momentum in gradient descent?
The Teach by Doing ML series is for ML foundations with a mix of theory + practical. If you want a basic intro in ML, you should start with that series. I specifically started this series because adding it into the "Teach by Doing ML" would be a bit of a diversion, since this series will go in a lot of depth with respect to Neural Networks. If you want a basic understanding with practical knowledge of neural networks, that will be provided in the Teach by Doing ML series. In the Teach by Doing ML series, the Momentum lecture will be followed by practical implementation of the entire NN in Python, It won't have the theoretical details we are covering here.
Yeah I tried to follow Andres video couple of times. Only understood at high level. Details were difficult to interpret. Thanks for doing this. One request though. Could you please move your camera video popup to another place on screen which does not hide code?
Hi, at 6:09 you mention a paper that you will follow, adding that you will share it right now. I and several other commenters are unable to find a link to any such file. Please share it again :-) Thank you for a great series.
I have just learned the basics of Python, is this too much too soon? I don't want to burn out. Can anyone speak to the prereqs to be able to follow all this amazing content?
I also am just learning the basics, but since my goal is learning machine learning I think it's a good idea to get exposed to that as early as I can, from there you can figure out what more you need to learn in python to accomplish your main goal.
For anyone - even children - wanting to learn about neural networks from scratch, I can also warmly recommend David Shiffman's The Coding Train seriens on the topic. th-cam.com/play/PLRqwX-V7Uu6aCibgK1PTWWu9by6XFdCfh.html
My mouth was just opened for a minute when he said he did phd in MIT and now he is giving his knowledge for free🤯
Finally, someone came up with this!! Really really grateful to you for this initiative. I hope you continue this playlist and teach every bit of Deep Learning from scratch.
These are all from a master's hand! Somehow writing them and having all your mind and body moving and connecting to that pen to move these ideas into images, charts and graphs is more cognitive than just really fancy computer generated graphics. It really goes into your mind!
This video very help me to learn fundamental theory of mathematics inside the Neural Network, hope u always healty. Thankyou so much Prof Raj Dandekar🙏🙏🙏🙏
Very grateful for this very in-depth and easy to follow. Brilliant!
Thanks a Lot! Really needed your channel and this series as my exams are about to get over, and I needed to start with my ml journey
Excellent video, Dr. Raj. Loved it. Please update the playlist.
Thanks for sharing.
It is very informative for me and I really like your hand writing and drawing of graphs.
Very well done ✅
Just what I was looking for. Thanks for making this video.
Thank you so much, This is exactly what i’ve been looking for.
Thank you from my heart for this initiative
Freakin 35 videos?,
Uff I really need more hours in my day,
And it's really nice to see from scratch ml explained in very detail like this,
Definitely on my to-do list to try these out
Thank you for this amazing series. It really helps in in-depth understanding of the Neural Network along with coding.
I am currently at lecture 5, and wanted to point out that Lectures need to be in order (ascending order) of Lecture numbers in the playlist. Secondly the superimposed camera should only capture your image, not extra things like room (Lecture slide/code get behind you). I hope these changes will enhance user experience.
Thanks for the feedback! All lecture names have been changed to reflect the order of lectures such as Lecture 1, Lecture 2 etc. The camera part has been implemented for all lectures after lecture 3.
Really love this content. I don't like black boxes either.
Very Grateful🙏... Thank you so much for providing such high quality content... This is literally🔥🔥🔥
Thanks you for sharing your expertise. Highly appreciate.
Hi, thanks for this! please take this series towards its logical conclusion i-e towards the latest LLMs/ GenAI as I feel no one explains concept+code in a structured way from scratch and lots of gaps in understanding are left. Also, your picture frame is hiding the code part. Hope it is resolved in the next videos. Thanks again!
This is fantastic!
Nice explanation about the initial basics of ANN. Could suggest some ANN Books?
amazing video, But please see, your video is covering the codes which is bit frustrating, but overall amazing video!
Outstanding...
Hello Raj, just one small suggestion, the video-over-video overlay is kind of hiding the main content, can you please move this to any suitable location while explaining. please check between 23-26 mins what I am talking about as the captions are also overlapping the code. Thanks again for your efforts! waiting for more learning and hands-on on this series.
Fixed this in latter videos!
@@vizuara thank you! 👍
I'm a software developer with no background in ML. Really interested to see this series unfold. I'd love if you could move the face cam display a little more towards the left of the screen as it hides the content.
I did exactly that in the next video! Coming out today at 10 am.
Is this series separate from "Teach by Doing ML"? I'm a little confused because the neural network basics video is also in that playlist. Is this video a continuation of your last video on momentum in gradient descent?
The Teach by Doing ML series is for ML foundations with a mix of theory + practical. If you want a basic intro in ML, you should start with that series. I specifically started this series because adding it into the "Teach by Doing ML" would be a bit of a diversion, since this series will go in a lot of depth with respect to Neural Networks. If you want a basic understanding with practical knowledge of neural networks, that will be provided in the Teach by Doing ML series. In the Teach by Doing ML series, the Momentum lecture will be followed by practical implementation of the entire NN in Python, It won't have the theoretical details we are covering here.
Yeah I tried to follow Andres video couple of times. Only understood at high level. Details were difficult to interpret. Thanks for doing this. One request though. Could you please move your camera video popup to another place on screen which does not hide code?
is this understanding correct?
1. input has weights associated
2. bias is associated with neuron, i.e. neuron's property
Can you please share the link of the whiteboard for all the videos?
Hello Dear Dr. Raj,
Can you share this e-book ?
Hello instructor. Could you ahare that book? Thanks.
completed
Hi, at 6:09 you mention a paper that you will follow, adding that you will share it right now. I and several other commenters are unable to find a link to any such file. Please share it again :-) Thank you for a great series.
I have just learned the basics of Python, is this too much too soon? I don't want to burn out. Can anyone speak to the prereqs to be able to follow all this amazing content?
I also am just learning the basics, but since my goal is learning machine learning I think it's a good idea to get exposed to that as early as I can, from there you can figure out what more you need to learn in python to accomplish your main goal.
Concept bhi day day bhai. neuron hota kaya h or weight hoty kaya hien
Can you please share the NNFS.pdf file as well.?
🎇
It seems there is no code in the attached Juypter notebook
This has been fixed. Do check
15, 16, 17 and 19 videos are missing
They are all scheduled to be released in the next 4-5 days
@@vizuara really good, anyone can understand the way you explained
For anyone - even children - wanting to learn about neural networks from scratch, I can also warmly recommend David Shiffman's The Coding Train seriens on the topic. th-cam.com/play/PLRqwX-V7Uu6aCibgK1PTWWu9by6XFdCfh.html
Diavlo, PHD en el MIT. ☠☠☠
what is the nnfs.pdf book you are following?
Hi, I can't seem to find the nnfs pdf file either. Please share and provide link.
@@h4tt3n I am asking the same 😕