dear sweet baby jesus. You just gave my brain a breakthrough, this is probably the best introductory course on Neural Nets on the internet. Thank you Luis!
00:00 What is machine learning ? 2:22 Gradient descent 5:07 Neural network 10:11 logistic regression 12:28 Probability 14:57 Activation Function 19:56 Error function 22:34 Node(Neuron) 24:07 None liner regions 31:22 Deep neural network
why did I not find this video before!!! this is amazing, Luis. You are clearly a very talented teacher, thank you so much. Omg those Stanford and MIT lectures are making so much more sense.
I am at the loss of words to describe how helpful it was to understand the basics of neural networks. For me, neural networks are not scary anymore. Thank You!
You have explained so many foundational insights and distilled multiple concepts in deep learning and artificial neural networks all in one video of just around half an hour. You are amazing. I feel like have a unique perspective on deep learning now and can grasp higher concepts. Thank you Luis.
I'm just going to reiterate what I've already said in other comments: your talent to turn very complex subjects into visual representations and easy words is truly inspirational. If Unis had more people like you there would be scientists and this would be a better world. Can you please story-tell us around Feature Engineering?
Thank you Alessia! Yes, I'm definitely due to make a feature engineering one, hopefully soon! In the meantime, this video touches on it: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-aDW44NPhNw0.html
Looked sooo many "wannabe easy" videos on this stuff which all skip essential parts like I learned now - I come to think that they don't even understand it at such a level as you did... - now i finally understand it! Please continue your videos!!!!
I reserved my first comment on RU-vid for something like this. I second my thoughts with Joao Sauer. This a testimony to how human mind is still the most intelligent computer that could help translate a complicated subject to a simple model. Thanks Luis Serrano. This is very helpful. Appreciate your effort in putting this together.
Thank you so much for posting this video and help me a lot. When i was student, the instructor focus on backpropagation derivation and i never fully understand the concept of neural networks. Thank You Again!!
I am sorry, but I can not stop myself to praise Luis. He has gene of explain complex things. I recommend his videos to all new learners. Nobody able to explain such clear.
Here in 2024 and been trying to wrap my brain around this stuff for a couple years. Your vids have absolutely made it all click. Well done and many thanks!
These intro courses on you channel, are too good. Before some months, I started ML, without learning these basics, this was harder to jump on mathematics behind ML. Now, I know how and why those formulas were applied in ML problems.
this was THE BEST lecture that has explained neural networks. thank u!! Very Clear Explanation! I feel comfortable moving forward with this topic! Well done ✔ Thanks best regards from Egypt 😍
how well a person can explain complicated things to be easily understood shows the level of cognitive intelligence he possesses. Did i mention Luis you are one of those people ?
You nailed it.. Having talent is important but need a lot of intelligence to explain it. You really made my life easy.. Awesome...please go ahead and teach as much as you can...we are thankful to you sir..
Great video. Thanks so much. This is the best explanation of neural networks I ever watched. Conceptually speaking is the “filter” of a convolutions neural network analogous to the hidden layers.
That was brilliant! Thank you so much. As a statistician I understand all the bits that go into making the neural network, your explanation was the most intuitive I have ever seen of how they all come together to make a neural network. You know those moments where you have spent ages trying to figure something out then something just clicks, and you say! "Ahhh, is that it? It is so simple". Well, your video just gave me one of those moments. I wish I could give you more than one like.
Deat Mr Serrano, I am a electrical engineer student in germany who tries to get more knowledge on the field of artifical intelligence and its sub - and subsubtopics machine learning and deep learning. This video gave me pretty good imagination of the mathematical formulars behind all the magic and that's why I want to thank you, thank you !
On a second thought, it would be great if you could come up with an example (like ML Intro video) showing NN in use. Maybe you can include concepts for WEIGHTS, FEEDBACK LOOP, TERMINATION CRITERIA and so on. Once again many thanks for creating the video. Really appreciate all your efforts!
Thank you for the suggestion, Nilesh! Yes, working on a few more videos, including one where the training part is explained in more detail. Feel free to send any other suggestions you may have, always open to new ideas!
Thanks for turning a research matter into a cartoon-like story wherein anyone's curiosity is developed and is forced to see each of your well dedicated videos. Hats off to your determination to help the research community.
Thank you for your kind words Sandeep! I enjoy understanding things in a pictorial way, and I'm glad that more people in the research community also feel this way.
i am from bangladesh i was search for neural networks easy expalined video......few days. i found your video. and its realy best explanation video......first 2 example is best to explained about neural networks
What a great entrance for a complete newbie to the the topic!! Especially the beginning with the cake helped so much with actually getting behind the idea before understanding HOW it actually works. Many others missed that point completely ....
This was so unbelievably awesome thank you. I've been struggling to understand this stuff for months and your video made it completely obvious. Thank you!!
Thank you for the opportunity to gain some understanding of a subject which looks to be completely outside a completely unrelated profession but may be something that fascinates a few of us in terms of what potential it may hold if we think outside of the box. Thank you for your time and effort.
Thank you Rhonda! I find this fascinating too, and machine learning is definitely a field that many professions can use, so I think it should be accessible to people with all types of backgrounds, not only math/cs.
I like the way it is presented this complicated topic. Very effective method of simplifying the cryptic topics. Please do post such videos. Really appreciate it.
I really had a hard time grasping the basic concepts of the neural network by reading a couple of tutorial and articles on this topic. But this video just blew my mind. It is simply the best. Thanks a lot @luis for this awesome explanation.
Thank you for your message, Safa, it is an honor to hear that. Best of luck in your learning process, and if you have any questions or suggestions, let me know! :)
Dear Luis, great work. There are no words that can be described by any neural network to than what you have been doing and keep it up. May God bless you with everything you need in life.
Yes man. Great job teaching. I also enjoy when you share your mind through metaphors, for example, the line in the sand, and the magnifying glass. Thank you for making the video.
Superb presentation, most people will be wondering what hidden layers are and what part they play, this clears it up of course the next question is how does one choose x or y hidden layers
This is so good, it illustrates clearly now an n-dimensional arbitrary shape in the data can be defined by the lines specified by neuron pairs. That shape specifies something the enclosed/defined data have in common. I loved it.
Jabril recommended this to me. I first went to giant’s neural network series and learned the bare basics. Then naturally the question came to my mind, why would we need multiple (hidden) layers of neurons. This video just blew that doubt to smithereens. Thank You!
I can only echo the other comments; what an outstanding introduction to an often obscurely taught area!!! Thank you so much, Luis! Keep up the good work.
It is really friendly as per the title. The examples used are very appropriate and excellent. The explanation is fantastic, anyone with the interest of learning can understand. Using these simple examples I can easily make my students understand the concept of machine learning. Thanks for your good service.
Luis, very well explained. I have seen many articles and video on ML and NN. Your video provides a "deep" understanding of the basics of Neural Networks and provides insights in solving problems with them. Thank you.
Maybe Im missing the simplest point of it all, at 20:37 how do we get the log values ? E.g. how does -log(0.1) become 2.3 (instead of 1?) and how do we get the 4.8 as a sum? What am I missing? :) EDIT: I just realised it's a natural log (so ln(0.1) = 2.3 instead of log10)...I'll leave this up here in case someone else missed it as I did instead of deleting the question. Otherwise the best explanation on the topic ever! Thanks!
Perfect! The best explanation seen so far! Easy to understand. Please build up on this video some more examples. Then please give a practical excercise in order to make this lesson being more useful. (for example with training data?) thank you so much!
I am going to press like before I watch the video because I just love how Luis explains in a friendly way. I watched the other video about machine learning and I fell in love with how machine learning works, I am currently taking courses in Udemy and Udacity. Luis, if you live around the Bay Area I would like to visit you and say thank you in person.
