Hats off, I am a PhD student, and I worked on NLP, ML and text analytics, in the last semester of my PhD I am turning to deep learning for my post doc research, and I needed background information on deep learning. Also in my last project somehow I managed to apply deep learning simple classifier, but that instinct to theoretically and technically understand background of deep learning was missing. I read articles, videos etc. a lot but man your videos on deep learning concept is really fulfilling my instinct up till now. Hats off to you Bro. Thank you for your vision of education and these helpful tutorials.
Very structured and organic build up of concepts, not throwing a bunch in a short timeframe down your throat praying you gobble it up. I appreciate your hard work behind the animations too.Keep it up!
I do get valuable information from youtube now and then. However, I did not expect deep learning tutorials to be explained in such simplicity yet highly informative as well. Machine Learning and Deep Learning videos on this channel are highly recommended. Thank you for such contents.
sir what ah explanation , it seems so easy to learn deep learning,carry on your winning momentum , hope you become one of the great teachers in data science🔥🔥🔥🔥🔥🔥🔥🔥🔥
I really loved the easy explanation given by your sir. I wish I'd found this series earlier, but will watch the series from now on. Thank you for your efforts.
You are awesome. Complex topic explained so clearly that they just stick to brain. These lectures are of the highest quality. Thank you for sharing your knowledge and for free!
Thank you very much! Such a great explanation. Thank you for explaining the pitfalls in the activation functions - it is for the first time I hear them.
Hi Sir, Amazing, I watched many videos in UA-cam regarding Deep Learning or Data science, But i failed to find this type of help, lectures, mentoring. Hats off. A bundle of thanks and prayers for you from Rizwan (Pakistan). Keep it up.
sir, you give a good concept of deep learning. Sir i am beginner and one my friend refer your deep learning lectures when i started your lectures i learn so much from it. Sir keep it up for future, thank you sir again..
There is no better explanation i've come across when it comes to Data science/ Machine Learning/Deep Learning, it's a shame that big e-learning companies like edureka are just copying content as mentioned by dhaval sir in one of his recent videos.
Your explanations are always classic and very detailed even undergraduate student can start learning DL during his course. Keep it up sir....keep good health.
Use of ml algorithms train data with good accuracy if our data is small . When we have tons of Data , using deep learning can fix with good accuracy If the data is small deep learning cannot fix good accuracy
Also it's very difficult to handle a deeplearning model than a machine learning model. Also you might have noticed deeplearning models aren't quite responsive as compared to ML model as it requires a lot of time for training. Another thing you only go for deeplearning when you have tons of data's otherwise you'll end up overfitting your model.
Hello Sir, Thank you for your tutorials and I found them very interesting and easy. Previously I was very afraid of machine learning but now due to your simple explanations it became my favourite and interesting subject. I have a doubt regarding this activation function tutorial, we are implementing hidden layers as the real world features have a non-linear relationship with the output but if activation functions like ReLU are used which is a linear function how does it capture the non-linearity of the features ? Also another question if I use either sigmoid or tanh function for hidden layers and not for output layer and if there is no vanishing gradient problem for a case, how is it capturing the linking patterns of features/inputs since for any problem we are adjusting it to sigmoid or tanh function. Am I missing something, could you please help me with both the questions sir please.
00:00 Activation functions are necessary in neural networks 02:04 Activation functions are necessary for building non-linear equations in neural networks. 04:06 Step function and sigmoid function are activation functions used in classification 05:57 Use sigmoid function in the output layer and 10h function in all other places. 07:54 Derivatives and the problem of vanishing gradients 10:02 The most popular activation function for hidden layers is the sigmoid function. 12:04 Sigmoid and tanh functions are used to convert values into a range of 0 to 1 or -1 to 1 respectively. 14:30 Positive values remain the same, negative values become zero, leaky value function multiplies input by 0.1
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Number of views is not doing justice to the quality of content that is created. Learning two weeks content in hardly 20 minutes. Thanks
I appreciate you leaving a comment of appreciation
Hats off, I am a PhD student, and I worked on NLP, ML and text analytics, in the last semester of my PhD I am turning to deep learning for my post doc research, and I needed background information on deep learning. Also in my last project somehow I managed to apply deep learning simple classifier, but that instinct to theoretically and technically understand background of deep learning was missing. I read articles, videos etc. a lot but man your videos on deep learning concept is really fulfilling my instinct up till now. Hats off to you Bro. Thank you for your vision of education and these helpful tutorials.
May I please get your email ID
Sir do we need Deep Learning for NLP??? Please help me
@@aadityaprashantwaghulade9666 YES
@@aadityaprashantwaghulade9666 If you ask that question then I don't really think you need it
@@aadityaprashantwaghulade9666 yeah right
Very well articulated, I searched the whole web, nobody explained these concepts in such simple way, without any confusion!!! Thank you
Very structured and organic build up of concepts, not throwing a bunch in a short timeframe down your throat praying you gobble it up. I appreciate your hard work behind the animations too.Keep it up!
I do get valuable information from youtube now and then. However, I did not expect deep learning tutorials to be explained in such simplicity yet highly informative as well. Machine Learning and Deep Learning videos on this channel are highly recommended.
Thank you for such contents.
I am watching your video since beginning. All is amazing Sirji.
sir what ah explanation , it seems so easy to learn deep learning,carry on your winning momentum , hope you become one of the great teachers in data science🔥🔥🔥🔥🔥🔥🔥🔥🔥
I really loved the easy explanation given by your sir. I wish I'd found this series earlier, but will watch the series from now on. Thank you for your efforts.
All the best Shreya
Shreya Tumi kamon a6o
The best UA-cam teacher!
You are awesome. Complex topic explained so clearly that they just stick to brain. These lectures are of the highest quality. Thank you for sharing your knowledge and for free!
BOSS BOSS, one of the best pedagogue
Thank you very much! Such a great explanation. Thank you for explaining the pitfalls in the activation functions - it is for the first time I hear them.
😊👍
Thank you once again for making Machine Learning simple...God bless.
Thank you so much .... The way you are teaching big big concepts with so much of easy understanding... it's really very very good... keep going....
I am happy this was helpful to you.
i am getting more attracted to words deep learning by ur explaination wt a explaination great sir hatsoff
GREAT explanation ..this video and all the others in the playlist.
Thank you so much sir for all the videos not just this one.
Hi Sir, Amazing, I watched many videos in UA-cam regarding Deep Learning or Data science, But i failed to find this type of help, lectures, mentoring. Hats off. A bundle of thanks and prayers for you from Rizwan (Pakistan). Keep it up.
Thanks Rizwan for your kind words and glad these videos are helping you 😀
Best Video on UA-cam on this topic
Thank you! Your tutorials are helping me get started with CNNs for my research!
😊👍
this was extremely important, cleared my all doubts and now i think i m able to solve problems myself thank you so much, god bless you
Your videos are excellent. Your words and diagrams really help clarify the process. I have recommended your videos to fellow colleagues. Bravo 👍
wow this channel has a lot of crucial content. relu activation decreased my loss value from 0.04 to 0.003 even with half of training data!
please, do more videos like this, it's so good for my brain development🧨🧨🧨🧨
Brother u are a savior god bless you
sir, you give a good concept of deep learning. Sir i am beginner and one my friend refer your deep learning lectures when i started your lectures i learn so much from it. Sir keep it up for future, thank you sir again..
I want you know that you are wonderful. I really enjoy watching your tutorials 💟
maths parts must be known to the learner because to understand the problem statments , and your lecture help me a lot thanks ,,,,,,
Nice series of tutorials. Super easy and time-efficient explanations.
very nice videos....good work
your explaination is great!!!
Great explanation sir, I refer to all your videos for explanations.
Best explanation ever. Will share for sure
Glad it was helpful!
Thank you so much for all the tutorials! You are the Man!!
There is no better explanation i've come across when it comes to Data science/ Machine Learning/Deep Learning, it's a shame that big e-learning companies like edureka are just copying content as mentioned by dhaval sir in one of his recent videos.
Your explanations are always classic and very detailed even undergraduate student can start learning DL during his course.
Keep it up sir....keep good health.
👍😊
This is brilliant indeed Dhaval
Very useful....made it very easy...thank you
Glad it helped
big fan of this tutorials
Awesome explanation plz continue this series
Great explanation!
Excellent series, clearly explained Thanks.
love your tutorials ❤❤❤
thanks bro that really helped .
Thanks for the video .. wish you all the best
Really very intuitive indeed!🙏🙏🙏🙏
I am happy this was helpful to you.
Thanks you sir, now I am finding deep learning kinda easy
Glad to hear that
incredibly informative video sir, thank you
Just love the way you explain.
thanks mithlesh
SIR HOW YOUR CONCEPT IS TOO CLEAR ON ANY TOPIC? AWESOME Sir🙏🙏
Very nice and easy explanation sir.
Amazing teacher
Great work!
Thank you for such an amazing tutorial!
Glad it was helpful!
Very helpful video sir☺
Well explained sir...waiting for next video
😊👍
nice and clear explanations thanks
thank you so much i have learned so much
Thank you brother you explained it very well
it's really help full thanks
Understood well...thanks sir!!
super explanation sir!!
Glad you liked it
Great video! Thank you
Simply fantastic
Thanks a lot, sir for such an amazing tutorial
superb video sir 💕💕💕💕💕👍
Glad it was helpful!
Sir, what is the significance of traditional ML algorithms (eg: Linear Regression, Random Forest etc) if deep learning is becoming so popular?
Use of ml algorithms train data with good accuracy if our data is small
.
When we have tons of Data , using deep learning can fix with good accuracy
If the data is small deep learning cannot fix good accuracy
@@karthikc8992 Ohh
Thanks for the info ☺️
Also it's very difficult to handle a deeplearning model than a machine learning model.
Also you might have noticed deeplearning models aren't quite responsive as compared to ML model as it requires a lot of time for training.
Another thing you only go for deeplearning when you have tons of data's otherwise you'll end up overfitting your model.
amazing tutorial.....right to the point...thanks :)
Glad you liked it!
very nice concept
Sir u r amazing
Thanks for your explaniation
Glad it was helpful!
really amazing
thank you great work
Amazing!
Nice explanation 😎👍
Glad it was helpful!
Excellent
I like your all videos
And I like your comment Mayank :)
Hello Sir, Thank you for your tutorials and I found them very interesting and easy. Previously I was very afraid of machine learning but now due to your simple explanations it became my favourite and interesting subject. I have a doubt regarding this activation function tutorial, we are implementing hidden layers as the real world features have a non-linear relationship with the output but if activation functions like ReLU are used which is a linear function how does it capture the non-linearity of the features ? Also another question if I use either sigmoid or tanh function for hidden layers and not for output layer and if there is no vanishing gradient problem for a case, how is it capturing the linking patterns of features/inputs since for any problem we are adjusting it to sigmoid or tanh function. Am I missing something, could you please help me with both the questions sir please.
dhasu 🔥
00:00 Activation functions are necessary in neural networks
02:04 Activation functions are necessary for building non-linear equations in neural networks.
04:06 Step function and sigmoid function are activation functions used in classification
05:57 Use sigmoid function in the output layer and 10h function in all other places.
07:54 Derivatives and the problem of vanishing gradients
10:02 The most popular activation function for hidden layers is the sigmoid function.
12:04 Sigmoid and tanh functions are used to convert values into a range of 0 to 1 or -1 to 1 respectively.
14:30 Positive values remain the same, negative values become zero, leaky value function multiplies input by 0.1
Thank you!
Sir, thank you very much.
Glad it was helpful!
good tutorial
useful video
thank you so much.
well done
congrats for your lovely tutorial. is C++ being used for deep learning? or Python is the top list of industries for AI transformation.
Python rules the ML world. People use C++ but rarely
Sir please include softmax activation fuction also..can u give me brief of it...because i m using it in my project
Dear sir.. request to make videos on Boltzmann machines
This is really helpful. Could you please provide the slides?
thanku sir
sir thank u so much
Most welcome kiran.
thank you
Can you please tell something about Asymmetric Activation Function in Neural Network ?
can you please make a separate video on vanishing gradient.
sure will do
Sir, what about softmax activation function, only for multiple classification? any other activation function for time series analysis?
is there any activation function that does not suffer from vanishing gradient problem?
@2:52 Sir, can you explain in detail the non-linearity part of activation function
Can you also upload the presentation slide in the github link for quick occasional revision. Thank You