Deep Learning Crash Course for Beginners

Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code.
You'll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more.
✏️ Course developed by Jason Dsouza. Check out his KZbin channel: / jasmcaus
⭐️ Course Contents ⭐️
⌨️ (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
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[@balintpinter5202]

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.

[@monleyson8668]

Yeah me too..

[@CaptainRaav]

@@monleyson8668 any other video recom? in the topic of deep learning or python related?

[@claudioa.dmedina2020]

do not forget that you already have weights in your model of understanding neural networks, since you have seen other videos prior to this.

[@seanleith5312]

if you don't speak English, let someone read it. it bothers me a lot to hear the stupid accent.

[@wirotep.1210]

@@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

[@sumitlahiri209]

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.

[@robertoramos5649]

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

[@beowulf2841]

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.

[@Snair1591]

This was such a great refresher course. Everything I needed to recollect and on point. Great job and thank you!

[@mipmap256]

Wow, You almost covered everything need to know about DL. great work.

[@miguelcorreia6426]

Excellent introduction to the topic. Great slides, great explanation, right pace. Really good.

[@saagarregmi8954]

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!

[@scottterry2606]

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.

[@kd4pba]

OK, finally someone that understands how to teach this. Excellent pace and details.

[@samtabstab3636]

Loved it. Thank you Jason!!!

[@anchalrani6273]

I needed just this course a perfect one for revising all concepts in short time. Great work. Thank you.

[@yankomirov4290]

Just when I need it the most! What a great timing!

[@krishchauhan6472]

Same as me

[@aleksatancos4111]

I love your venom

[@kabhishek707]

these are the things exactly one want to know to do a project based on deep learning.

[@tobebuilds]

Awesome video, Jason. You've helped make this information accessible to thousands of new people.

[@samanthamenezes971]

Gud goin Jason ! Great content....Keep adding more courses..... Looking forward.

[@abnerober]

This is so deep and didactic at the same time! Thanks a lot for putting the effort to produce the video!

[@EthanWait]

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!

[@Hollyweed1]

Top quality lullaby. Slept like a baby 15 minutes in! 👌

[@saurabhparate6866]

🤣

[@dudemaster3000]

Litterally here to fall asleep

[@bibillyjay]

i came here to watch it dedicatedly this comment manipulated me😂

[@akashjain35]

This is gold! Brilliant job done by you guys.

[@karlobobiles2968]

Solid video! Easy to understand. Thank you 🙏

[@nadaabdelhamid-ju4bh]

I can't believe this material is published on KZbin for free. Best course I have taken on KZbin ever!!!!!

[@bandhandey2594]

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.

[@monicasoto4776]

You explained the concepts extremely well, thanks for this amazing video!

[@priyankasuresh97]

Thank you, this is one of the best deep learning tutorials for beginners

[@aruneminence]

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.

[@keerthanareddy95]

Perfect for a beginner like me. It made me fall in love with Deep Learning!!

[@hasnainfareed8555]

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.

[@user-lw3ri8us4w]

how did you end up doing? where are you now?

[@KumR]

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...

[@Pictor13]

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!

[@Pictor13]

[/finished to watch the video and add notes]

[@OstonCodeCypher]

⌨️ (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

[@boop1278]

Helo

[@OstonCodeCypher]

@@boop1278 Hola

[@-hikikomori-7191]

It's in the description though

[@OstonCodeCypher]

@@-hikikomori-7191 100%

[@user-vf7tu6nr4d]

Ctrl c Ctrl v from description or effort for nothing sorry

[@chloinger]

You've probably saved my grade in my university "Deep Learning" course!! Thank you! ❣

[@manojkl1323]

Supervised learning can be a regression as well. It's not only predicting the correct label (classification problem).

[@davidelizondo6271]

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]

[@jishnusingh6621]

its (0,1) not [0,1]

[@prashantjangid5751]

Best for Course for an new Deep Learning aspirants....Kudos to Jason Dsouza

[@deepalipandey4707]

Beautiful course , thanks for the content

[@syedmohammadshadab]

Very enlightening for beginners! Very nice voice-over! Thanks!

[@funskill-relaxationsounds7521]

My brain is really tired but this was very helpful. Addressed almost everything my lecturer mentioned in class

[@arnonymous8113]

This is the first time I actually understand a little bit about AI. Great work.

[@athinastewart7061]

Awesome video! Super comprehensive yet compact and simply explained

[@anggifirdiansaputra6615]

Keep up the good work🔥. Thank you😊

[@craftanddrawingideas1301]

Thanks bro. Appreciated.

[@harshdevmurari007]

TOO GOOD FOR REVISION OF DEEP LEARNING CONCEPTS FROM SCRATCH.....THANK YOU AWESOME CRYSTAL CLEAR EXPAINATION....

[@ShahxadAkram]

I'm a beginner and you just nailed it.

[@umangtiwari5644]

Probably the best video I've watched on deep learning.

[@DecisionForest]

Great video! I like the structure and the depth of knowledge shown in it.

[@saranggokte4165]

This was so fucking awesome. Thanks for doing this. Refreshed all my DL concepts.

[@wishIKnewHowToLove]

:)

[@paridhi._gupta]

Thanku so much for amazing content!!

[@omaressam9259]

Great effort!! Thank You.

[@Radical9535]

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.

[@eng.khalid9763]

Thank you very much for this interesting course. It took me about a week to complete it.

[@brunomartel4639]

is it okay to say that RNN "digest" data a little longer (as an analogy to feedback loops) so it can "spit" better results?

[@ivandiaz5549]

It´s crazy how much information this video has. Thanks:)

[@krishshah3974]

ikr

[@HK-fq6vh]

Thank you bro. Great one.

[@forrealitsme4438]

granddaddy of optimizers got me lol Great video btw!!

[@xXKIKOXx0]

21:11 why not just use multiple activation functions? if your goal was to find a specified output does running data through multiple "filters" be optimal? the argument I can think of is just "cost" like time to compute/etc. but what do you think about this theoretical?

[@lakshman200]

This video is really helpful, Thank you so much for the video!!!

[@s4j4dms15]

Man this was great thx a lot🙏🏻

[@moominawaheed1870]

Excellent course for the revision of all concepts of deep learning

[@cookiemaster1049]

Looks quite brief and interesting in depth lecture 👍👍

[@Julianosslv]

Thanks, man. I love you!

[@osmansafi4517]

Honestly the best course I came across at the moment

[@INTJ_poetics]

If I'm having a difficult time keeping up with the activation functions, what should I study to be better prepared for this tutorial?

[@beyonderaatrox1670]

Heard a lot about this but I'm gonna be honest I have no idea what it is. Let's get into it tho 🙂

[@antoniodesousabarroso7945]

Great job. Thank you.

[@jhonatanmaia5652]

I loved the video, its excelent. Is there a recommended model for time series?

[@meetkoriya]

use facebook prophet or NeuralProphet libraries

[@zs2769]

Very good for recapping the knowledge of Deep Learning. Thanks

[@brunomartel4639]

When you say that our validation size depends on the hyperparameters, you mean the hyperparameters that the algorithms has by default or the hyperparameters that we tuned?

[@mohammadajami8073]

Thank you it was really amazing course!!

[@damian4926]

Love this tutorial.

[@tirupathirao7166]

thank you so much for the course bro, it was fabulous

[@marcosantos9512]

Amazing video, thank you so much!!!!!!!!

[@daniyalkabir6527]

Concise and clear!

[@s.santhosh4668]

Sir i have one doubt i want to do the project on deep learning can i directly start with deep learning or i have to learn data science, machine learning and deep learning.

[@ankurmanna8094]

How to get transcript of this whole video into organised notes ? or atleast broken down into timestamps

[@nakulmali1413]

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

[@kalharpatel4784]

This is awesome!

[@egesarca9550]

Great course for beginners, thank you

[@varunbondalapati9264]

Thank you..!!!

[@claras.3600]

This is really well explained! Can you recommend a good book on the theories of Deep Learning? :)

[@krazzyvibestv8657]

Please try out Make the most of your mind by Tony Buzan

[@bestclips4525]

@@krazzyvibestv8657 what it has to do with computer deep learning??

[@petermueller8427]

Nice video!!!!! Very good work! Thx

[@harshkumarshrivastava8749]

Thanks you're great!!

[@akshay.041]

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...

[@aryannambiar8719]

Is there some way we can get access to the powerpoint you are using??

[@jamesmelton7637]

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.

[@reilley4297]

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

[@marketalpha5426]

For anyone getting started in ML, this is a must see. Thank you so much.

[@mfchong4598]

Better if shows some demo to do the 5 steps in Deep Learning Model. Any simple demo is good enough to highlight those steps.

[@osmansafi1972]

Any advanced version comming, such a fantastic course.

[@nardobarik4291]

Thanks your hardwork is recommendable

[@AlexLanglois1]

amazing for laymen! Wow!

[@someguy872]

Nice video man!

[@aadityamishra5066]

U didn't even watch the whole thing 🤦‍♂️

[@augustinecharly]

(7:19) Weight is assigned to a link and not a neuron right? And the bias term shifts the graph up or down, not left or right.

[@murrowboy]

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.

[@jeffy8657]

No clue what this is, i'm going to start through

[@nagendradevara1]

All the best !

[@egorbrandt]

keep us posted!

[@uhbjoigyhbvr6796]

@@nagendradevara1 better to be blind than the ability to see

[@user-mv4oh8yp1y]

14:36: this function represented by the graph is not linear, since it doesn't pass the origin.

[@lakshmipathiraodevalla4101]

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.

[@parvejshahlabib9167]

Exactly

[@briankapito7034]

Great video, thank you

[@pattiknuth4822]

(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".

[@mdfarhadhussain]

This is the BEST !!

[@wajahatiqbal5590]

10/10 , Thank you very much