For absolute beginners, go through this course 2-3 times, try to note important terms, topics, and processes, and then take every topic and go deep sequentially. I am happy that you have such a great mentor like him now. Best wishes all.
I watched a lots of deep learning tuturials before this, some of them were even twice as long as this and yet this explained the best and the most. Thank you for the awesome tutorial without ads for free. You are a hero.
@@claudioa.dmedina2020yes. Agree. This video best fit for one with certain background Else it overwhelms Don’t get me wrong, this video is very good. I will watch again
⌨️ (0:00) Introduction ⌨️ (1:18) What is Deep Learning ⌨️ (5:25) Introduction to Neural Networks ⌨️ (6:12) How do Neural Networks LEARN? ⌨️ (12:06) Core terminologies used in Deep Learning ⌨️ (12:11) Activation Functions ⌨️ (22:36) Loss Functions ⌨️ (23:42) Optimizers ⌨️ (30:10) Parameters vs Hyperparameters ⌨️ (32:03) Epochs, Batches & Iterations ⌨️ (34:24) Conclusion to Terminologies ⌨️ (35:18) Introduction to Learning ⌨️ (35:34) Supervised Learning ⌨️ (40:21) Unsupervised Learning ⌨️ (43:38) Reinforcement Learning ⌨️ (46:25) Regularization ⌨️ (51:25) Introduction to Neural Network Architectures ⌨️ (51:37) Fully-Connected Feedforward Neural Nets ⌨️ (54:05) Recurrent Neural Nets ⌨️ (1:04:40) Convolutional Neural Nets ⌨️ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model ⌨️ (1:08:23) 1. Gathering Data ⌨️ (1:11:27) 2. Preprocessing the Data ⌨️ (1:19:05) 3. Training your Model ⌨️ (1:19:33) 4. Evaluating your Model ⌨️ (1:19:55) 5. Optimizing your Model's Accuracy ⌨️ (1:25:15) Conclusion to the Course
Some (hopefully constructive) annotations to improve the video for better clarity: * 6:33 - "Channels have weight". And the slide matches it. But 7:08 says the weight is something of a neuron (how important is the neuron, rather than how important is the relationship). I think it is confusing; aren't weights a property of the relationship/channel, in graph theory? * inside slides that list advantages/disadvantages you might use the color red for disadvantages, and not just always green to highlight terms. Ex. "small" at 29:08. * I like the examples of descending the Everest, and the one about memorising songs. * 40:21 - the slides disappears till 40:57 * same at 52:01; till 53:11 * 58:15 - "sometimes you may find the ???? depicted over time" (the automatic subtitles don't get it either) * 1:04:17 - audio says "input gate, output gate, and a forget gate"; but the slides shows "Update, Reset & Forget gates". * 1:10:30 - "although if you are interested I'll leave them in the notes below"; yet, it would be useful if a list (or a link to more info) could be added in the description. These are the major notes that I think should be fixed, for better clarity. Anyway it is well made, a good explanatory overview of the neural networks world, that I had no idea about it. It was ok to understand for me, if I skip on the name of the specific algorithms (that in the end are implementation details). But I already got some basic knowledge of statistics & data-analysis, and about graph theory. My only doubt is if others that never dig in those topics can follow this video as well. Keep up with this interesting contents! Thanks for your time and effort!
Short and crisp overview of deep learning with cool intuitions. This is the only video that I will recommend to anyone who wants to start with deep learning and even machine learning in general.
After watching a lot of tutorials or courses about deep learning, i can truly say this is probably the best! Everything organized and clear. Congratulations, it will help us a lot! Thank you
This is honest to god a great lecture and perfect for introducing deep learning! I hope there’s another video in the future that shows some light programming.
This is a way better introduction than a University's Introduction to Deep Learning Course for beginners. While the university assumes some ML and computer science background before starting DL courses, this video is for complete beginners. I appreciate it!
Great job! As a FYI, when I used to teach, by the time I got to ANN's, I'd already covered statistical modeling. So it was always an "a-ha!" moment when I'd ask the class if they remembered the sigmoid function from before ... and that all the sigmoid functions acted like mini logistic regression models.
for those examples we can assume all possible directions until the ball moves as for "dog" we can assume all statements/questions are accurate until more content is provided at least that would be a temporary solution.
Hi Jason, Nice video,. The range for the si gmoid function (16:24 on your video) is not [0,1] but rather ]0,1[ as a 0 and a 1 can only be obtained towards +-infinity. Same applies to the hyperbolic tangent ranging from ]-1,+1[ and not from [-1,+1]
Thank You for this, this is my 2nd day into this field, and I think I know the big picture of how it all works and learnt around a month of stuff in a day or two.
Nice introduction. Some of the ideas probably should be reigned in a bit. For example, people who study learning do not think machine learning models how the human brain learns at all. Calling neural network nodes "neurons" misleads people into thinking you are actually trying to imitate a neuron instead of just using a software node. Stating that activation functions are non-linear is not always correct - in fact the equation you showed when you said that looks like a linear sum of terms. All this doesn't take away from the work you've done to make these ideas accessible. It's a good intro - but hopefully people understand there are some sweeping statements that might not hold up under close scrutiny.
Neuron and node are interchangeable enough in this context. Had 'neuron' not been used, a consumer of this course will still eventually run into each term. This course certainly wasn't the first to use 'neuron'. NNs do roughly model brain functions. Like Jason said, each works to identify patterns based on data. Jason also goes on to explain the case where linear activation functions are less than optimal. Of course there are exceptions, but beginners probably wouldn't be concerned with them
It was a good introductory course! Although there's lack of examples in the explanation of neural network part. But overall you can get the idea of how deep learning works.
Thank you for this amazing video. Honestly, out of all of the tutorials I've watched, it is the first time that someone explains it in such clear and understandable way. Again, thank you for sharing your knowledge!!!
0:10 The computer runs an algorithm on each players possible moves and picks the one that's closest to the king WITHOUT BEING EATEN. There is no ai in that. It's just iterating all the players and iterating all of their moves going from player to player. There are actually many moves to remember and remembering all of them on a move can be sometimes difficult when there are so many possibilities A computers algorithm in that framework is 100% correct all of of the time. In terms of the facial ai it's just objects which are outlined and marked with a set of attributes Trigonometry is really important here. Also business functions play here quite frequently It's also remembering situations and figuring out the best path I took at a path. A path is a set of steps I took. How many times has that played up before. What were the moves that won me the game at that path. That's the hard part. That's deep learning. That's path memory. Very difficult This is where you learn to flip the binary tree on its side and use them as open ports. Drawing it differently and analyzing is all we need to see. DNA Path memory
Such a great video - the explanations and pace were just perfect. I'm sure to use this video to review concepts time and time again... this was such well-put-together beginner material. Thank you so much for making this available for free! You are incredible!
The output shouldn't be right or wrong, it should be a variable no? Because not all input we receive is binary. If anything it should be on a scale of 1-10, 1 being very negative (lots of adjustments to neurons) and 10 being perfect and should increase the weight in the future.
I found the fake news detection statement amusing. This was clearly before we realised that every side of an issue has fake news (and some truths are just not available on any network because the data regarding them is deleted). If created today I would suggest the software would be used to determine the truth according to the clients parameters and specifications. Additionally the software would need to be directed to what are 'reliable sources' or otherwise risk verifying undesirable information. Sorry for overly discussing this issue. This video was very interesting, thank you.
It a wonderful course that I've ever seen on youtube s about DL. Thanks a lot, Jason for making this one. :). Expecting more vids from you to change lives.
Thanks for your all videos. But I request you please upload two different videos of 6-7 hour on Calculus and Linear Algebra required for Machine Learning and Data Science
Is there a way to have access to the slides used in the video? It would be really helpful for those who want to revise everything and doesn't have to go through the entire video.
Excellent.....please upload the very basic idea of machine learning and artificial intelligence.... it's too useful for us like intern and upcoming future...please upload freecodecamp...
"This function is therefore perfect to use for this context! Or so you would think." *proceeds to explain trade-off and subsequently destroy my dreams*
For absolute beginners, go through this course 2-3 times, try to note important terms, topics, and processes, and then take every topic and go deep sequentially. I am happy that you have such a great mentor like him now. Best wishes all.
I watched a lots of deep learning tuturials before this, some of them were even twice as long as this and yet this explained the best and the most. Thank you for the awesome tutorial without ads for free. You are a hero.
Yeah me too..
@@monleyson8668 any other video recom? in the topic of deep learning or python related?
do not forget that you already have weights in your model of understanding neural networks, since you have seen other videos prior to this.
if you don't speak English, let someone read it. it bothers me a lot to hear the stupid accent.
@@claudioa.dmedina2020yes. Agree. This video best fit for one with certain background
Else it overwhelms
Don’t get me wrong, this video is very good. I will watch again
⌨️ (0:00) Introduction
⌨️ (1:18) What is Deep Learning
⌨️ (5:25) Introduction to Neural Networks
⌨️ (6:12) How do Neural Networks LEARN?
⌨️ (12:06) Core terminologies used in Deep Learning
⌨️ (12:11) Activation Functions
⌨️ (22:36) Loss Functions
⌨️ (23:42) Optimizers
⌨️ (30:10) Parameters vs Hyperparameters
⌨️ (32:03) Epochs, Batches & Iterations
⌨️ (34:24) Conclusion to Terminologies
⌨️ (35:18) Introduction to Learning
⌨️ (35:34) Supervised Learning
⌨️ (40:21) Unsupervised Learning
⌨️ (43:38) Reinforcement Learning
⌨️ (46:25) Regularization
⌨️ (51:25) Introduction to Neural Network Architectures
⌨️ (51:37) Fully-Connected Feedforward Neural Nets
⌨️ (54:05) Recurrent Neural Nets
⌨️ (1:04:40) Convolutional Neural Nets
⌨️ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model
⌨️ (1:08:23) 1. Gathering Data
⌨️ (1:11:27) 2. Preprocessing the Data
⌨️ (1:19:05) 3. Training your Model
⌨️ (1:19:33) 4. Evaluating your Model
⌨️ (1:19:55) 5. Optimizing your Model's Accuracy
⌨️ (1:25:15) Conclusion to the Course
Helo
@@weyoflife Hola
It's in the description though
@@-hikikomori-7191 100%
Ctrl c Ctrl v from description or effort for nothing sorry
Some (hopefully constructive) annotations to improve the video for better clarity:
* 6:33 - "Channels have weight". And the slide matches it. But 7:08 says the weight is something of a neuron (how important is the neuron, rather than how important is the relationship). I think it is confusing; aren't weights a property of the relationship/channel, in graph theory?
* inside slides that list advantages/disadvantages you might use the color red for disadvantages, and not just always green to highlight terms. Ex. "small" at 29:08.
* I like the examples of descending the Everest, and the one about memorising songs.
* 40:21 - the slides disappears till 40:57
* same at 52:01; till 53:11
* 58:15 - "sometimes you may find the ???? depicted over time" (the automatic subtitles don't get it either)
* 1:04:17 - audio says "input gate, output gate, and a forget gate"; but the slides shows "Update, Reset & Forget gates".
* 1:10:30 - "although if you are interested I'll leave them in the notes below"; yet, it would be useful if a list (or a link to more info) could be added in the description.
These are the major notes that I think should be fixed, for better clarity.
Anyway it is well made, a good explanatory overview of the neural networks world, that I had no idea about it.
It was ok to understand for me, if I skip on the name of the specific algorithms (that in the end are implementation details).
But I already got some basic knowledge of statistics & data-analysis, and about graph theory. My only doubt is if others that never dig in those topics can follow this video as well.
Keep up with this interesting contents! Thanks for your time and effort!
[/finished to watch the video and add notes]
Short and crisp overview of deep learning with cool intuitions. This is the only video that I will recommend to anyone who wants to start with deep learning and even machine learning in general.
I can't believe this material is published on UA-cam for free. Best course I have taken on UA-cam ever!!!!!
After watching a lot of tutorials or courses about deep learning, i can truly say this is probably the best! Everything organized and clear. Congratulations, it will help us a lot! Thank you
This is honest to god a great lecture and perfect for introducing deep learning! I hope there’s another video in the future that shows some light programming.
Top quality lullaby. Slept like a baby 15 minutes in! 👌
🤣
Litterally here to fall asleep
i came here to watch it dedicatedly this comment manipulated me😂
True😂😂
OK, finally someone that understands how to teach this. Excellent pace and details.
This is a way better introduction than a University's Introduction to Deep Learning Course for beginners. While the university assumes some ML and computer science background before starting DL courses, this video is for complete beginners. I appreciate it!
these are the things exactly one want to know to do a project based on deep learning.
Great job! As a FYI, when I used to teach, by the time I got to ANN's, I'd already covered statistical modeling. So it was always an "a-ha!" moment when I'd ask the class if they remembered the sigmoid function from before ... and that all the sigmoid functions acted like mini logistic regression models.
Just when I need it the most! What a great timing!
Same as me
I love your venom
TOO GOOD FOR REVISION OF DEEP LEARNING CONCEPTS FROM SCRATCH.....THANK YOU AWESOME CRYSTAL CLEAR EXPAINATION....
Wow, You almost covered everything need to know about DL. great work.
True
This was such a great refresher course. Everything I needed to recollect and on point. Great job and thank you!
Thank you, this is one of the best deep learning tutorials for beginners
Perfect for a beginner like me. It made me fall in love with Deep Learning!!
for those examples we can assume all possible directions until the ball moves as for "dog" we can assume all statements/questions are accurate until more content is provided at least that would be a temporary solution.
Hi Jason, Nice video,. The range for the si
gmoid function (16:24 on your video) is not [0,1] but rather ]0,1[ as a 0 and a 1 can only be obtained towards +-infinity. Same applies to the hyperbolic tangent ranging from ]-1,+1[ and not from [-1,+1]
its (0,1) not [0,1]
Good one. If someone has finished an end to end training, this one serves as a quick refresher.. And at the end this is what one would remember...
Thank You for this, this is my 2nd day into this field, and I think I know the big picture of how it all works and learnt around a month of stuff in a day or two.
how did you end up doing? where are you now?
absolute amazed by the potential of deep learning. love this vid.
Nice introduction. Some of the ideas probably should be reigned in a bit. For example, people who study learning do not think machine learning models how the human brain learns at all. Calling neural network nodes "neurons" misleads people into thinking you are actually trying to imitate a neuron instead of just using a software node. Stating that activation functions are non-linear is not always correct - in fact the equation you showed when you said that looks like a linear sum of terms.
All this doesn't take away from the work you've done to make these ideas accessible. It's a good intro - but hopefully people understand there are some sweeping statements that might not hold up under close scrutiny.
Neuron and node are interchangeable enough in this context. Had 'neuron' not been used, a consumer of this course will still eventually run into each term. This course certainly wasn't the first to use 'neuron'.
NNs do roughly model brain functions. Like Jason said, each works to identify patterns based on data.
Jason also goes on to explain the case where linear activation functions are less than optimal. Of course there are exceptions, but beginners probably wouldn't be concerned with them
You've probably saved my grade in my university "Deep Learning" course!! Thank you! ❣
I'm a beginner and you just nailed it.
Excellent introduction to the topic. Great slides, great explanation, right pace. Really good.
It was a good introductory course! Although there's lack of examples in the explanation of neural network part. But overall you can get the idea of how deep learning works.
This is the first time I actually understand a little bit about AI. Great work.
I needed just this course a perfect one for revising all concepts in short time. Great work. Thank you.
Very enlightening for beginners! Very nice voice-over! Thanks!
Thank you for this amazing video. Honestly, out of all of the tutorials I've watched, it is the first time that someone explains it in such clear and understandable way. Again, thank you for sharing your knowledge!!!
This is so deep and didactic at the same time! Thanks a lot for putting the effort to produce the video!
It´s crazy how much information this video has. Thanks:)
ikr
Thanks for providing us with such quality material 😊😊
Supervised learning can be a regression as well. It's not only predicting the correct label (classification problem).
0:10 The computer runs an algorithm on each players possible moves and picks the one that's closest to the king WITHOUT BEING EATEN.
There is no ai in that. It's just iterating all the players and iterating all of their moves going from player to player.
There are actually many moves to remember and remembering all of them on a move can be sometimes difficult when there are so many possibilities
A computers algorithm in that framework is 100% correct all of of the time.
In terms of the facial ai it's just objects which are outlined and marked with a set of attributes
Trigonometry is really important here.
Also business functions play here quite frequently
It's also remembering situations and figuring out the best path I took at a path.
A path is a set of steps I took. How many times has that played up before.
What were the moves that won me the game at that path.
That's the hard part. That's deep learning. That's path memory. Very difficult
This is where you learn to flip the binary tree on its side and use them as open ports.
Drawing it differently and analyzing is all we need to see.
DNA
Path memory
Great information! Super well written. Clear and interesting!
Such a great video - the explanations and pace were just perfect. I'm sure to use this video to review concepts time and time again... this was such well-put-together beginner material. Thank you so much for making this available for free! You are incredible!
Awesome video, Jason. You've helped make this information accessible to thousands of new people.
Probably the best video I've watched on deep learning.
Umm...I am searching for something greater than the " Thank you so much"
but for now tqsm for such a osum video on DL.
goofy ahh abbrev
Excellent course for the revision of all concepts of deep learning
The output shouldn't be right or wrong, it should be a variable no? Because not all input we receive is binary. If anything it should be on a scale of 1-10, 1 being very negative (lots of adjustments to neurons) and 10 being perfect and should increase the weight in the future.
I found the fake news detection statement amusing. This was clearly before we realised that every side of an issue has fake news (and some truths are just not available on any network because the data regarding them is deleted). If created today I would suggest the software would be used to determine the truth according to the clients parameters and specifications. Additionally the software would need to be directed to what are 'reliable sources' or otherwise risk verifying undesirable information. Sorry for overly discussing this issue. This video was very interesting, thank you.
This was so fucking awesome. Thanks for doing this.
Refreshed all my DL concepts.
:)
Honestly the best course I came across at the moment
You explained the concepts extremely well, thanks for this amazing video!
is it okay to say that RNN "digest" data a little longer (as an analogy to feedback loops) so it can "spit" better results?
This is gold! Brilliant job done by you guys.
No clue what this is, i'm going to start through
All the best !
keep us posted!
@@nagendradevara1 better to be blind than the ability to see
This is awesome!
great intro to DL.. cheers
I loved the video, its excelent. Is there a recommended model for time series?
use facebook prophet or NeuralProphet libraries
Heard a lot about this but I'm gonna be honest I have no idea what it is. Let's get into it tho 🙂
(6:17) That formula is incorrect. There is only one bias value per neuron, not "n". Thus, the formula should have "b" not "b_i".
Best for Course for an new Deep Learning aspirants....Kudos to Jason Dsouza
It a wonderful course that I've ever seen on youtube s about DL. Thanks a lot, Jason for making this one. :). Expecting more vids from you to change lives.
Thanks for your all videos. But I request you please upload two different videos of 6-7 hour on Calculus and Linear Algebra required for Machine Learning and Data Science
My brain is really tired but this was very helpful. Addressed almost everything my lecturer mentioned in class
Gud goin Jason ! Great content....Keep adding more courses..... Looking forward.
Awesome video! Super comprehensive yet compact and simply explained
watch your career with great interest young Jedi
If I'm having a difficult time keeping up with the activation functions, what should I study to be better prepared for this tutorial?
Great intro to DL..cheers
Good course but tell us about the Advanced project
This is really well explained! Can you recommend a good book on the theories of Deep Learning? :)
Please try out Make the most of your mind by Tony Buzan
@@krazzyvibestv8657 what it has to do with computer deep learning??
Loved it.
Thank you Jason!!!
Better if shows some demo to do the 5 steps in Deep Learning Model. Any simple demo is good enough to highlight those steps.
Is there a way to have access to the slides used in the video? It would be really helpful for those who want to revise everything and doesn't have to go through the entire video.
Exactly
Any advanced version comming, such a fantastic course.
Great video! I like the structure and the depth of knowledge shown in it.
Very good for recapping the knowledge of Deep Learning. Thanks
This video is really helpful,
Thank you so much for the video!!!
Guys! The cover pick says LEANING not learning, correct it please.
Excellent.....please upload the very basic idea of machine learning and artificial intelligence.... it's too useful for us like intern and upcoming future...please upload freecodecamp...
granddaddy of optimizers got me lol
Great video btw!!
Great course for beginners, thank you
bang, bikin cara menghitung lossnya dong, untuk ngecek tingkat akurasi, f1 score, map, dll gitu
Solid video! Easy to understand. Thank you 🙏
14:36: this function represented by the graph is not linear, since it doesn't pass the origin.
this course is priceless
Yeah, its free so priceless is a good choice of word!😅
Excellent video, helped me a lot and translated into 7.5 pages of notes.
Can you please share your notes?
@@ameyakhot4458 It would be too much for a youtube comment, would it work as a google doc?
@@benjystrauss2524 Yes if it is possible
thank you so much This is very, helpful
Thank you very much for this interesting course. It took me about a week to complete it.
Thank you bro. Great one.
For anyone getting started in ML, this is a must see. Thank you so much.
i love u bro, keep it up🙂
I just started a course in Udemy about this. But i am all in this one too
this is much better lol
@@franklynxavier5643 i am sure of it :D
Will use everything available
best explanation for DL
Thanks bro. Appreciated.
Thank you..!!!
Thanks your hardwork is recommendable
Thank you it was really amazing course!!
Concise and clear!
"This function is therefore perfect to use for this context! Or so you would think." *proceeds to explain trade-off and subsequently destroy my dreams*
Beautiful course , thanks for the content
Thanks, man. I love you!
Please make full course on deep learning
that's what this course is there for duh