Neural Network For Handwritten Digits Classification | Deep Learning Tutorial 7 (Tensorflow2.0)
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- Опубліковано 17 лип 2020
- In this video we will build our first neural network in tensorflow and python for handwritten digits classification. We will first build a very simple neural network with only input and output layer. After that we will add a hidden layer and check how the performance of our model changes.
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Hello, why do we have 1875 steps in each epoch in 20:40 ? Where is this 1875 number coming from? It is not the 60000 training data size or nothing else we saw above that line?
Thank you so much by the way..,
Sir.. you deserve to teach the entire world machine learning.. you are an exceptional talent.
Just love the way you're making things pretty simple. A big shout-out to you.
Using 1 hidden layer (1000) & relu (remaining parameters untouched), I reached 0.9908 accuracy. Thanks a lot for this course.
Is it a evaluation score or training score?
love the have you figure out the issue of accuracy, That makes a huge difference.
from the Optimizers ['SGD','RMSprop','Adam','Adadelta','Adagrad','Adamax','Nadam','Ftrl'] with 5 Ephocs, Adamax got the highest score on Test samples. Ftrl got the lowest score.
Thank's a lot for making this wonderful playlist for Deep Learning.
exhilarating to see the inner workings of deep learning! thank you sir
This is the only lecture where I got a really great idea about neural network. I have seen many lectures no one explain each thing like you did. They just make the neural network
Well lectured..Have to listen twice to understand this session accordingto my perception..
Excellent Video.. I always follow the philosophy: First have something working with as much little theory as possible...and then play with parameters that increase curiosity and then dissect the theory..Makes life easy.. than the other way around..
Phani, exactly. I am following exactly same principal. There are many things in this tutorial such as loss, optimizer etc which remains mystery but I wanted to use that first and than unveil the mystery step by step :)
This is phenomenal, thank you so much! Truly a brilliant teacher. Been looking for a while, and this video summed up so much. You sir earned yourself a subscriber, and perhaps many more as I share your channel. Thank you for taking the time to do this.
Your course is more than explicit. Thank you so much for giving such efforts to share knowledge
this is the easiest and a must watch video to learn deep learning basics.
I will be always grateful to you sir
Again waiting for next ❤️
I love your way of explains something.
Sir, you are a born teacher. The way you represent complex topics in simpler way is truly amazing. I really admire your hard work for designing such wonderful courses. Thank you sir.
Great introductory video! Thanks a lot.
Awesome and clearly explained! Love it!
This is the most elegant solution of Hand-Written digits MNIST problem in the whole Internet! Thank you. I've learned so much! You have my sub and much respect!
Glad it was helpful!
Best Deep Learning Course I found On UA-cam, I mean you the gave the content that I was looking for.
What an amazing tutorial! Did a machine learning for the first time with so much clarity! Thank you so much sir
Hi, your videos are so insightful. I would like to request if you could include or do a video on kerasclassifier? Thank you so much!
Amazingly explained!! Appreciate your work sir. Big fan!!
you are SUPER , please continue ,also if you can explain deep reinforcement learning , i will be so grateful !!
Thank you so much for the great explanation!
By using relu in the hidden layer and softmax in the output layer , was able to achieve an accuracy of 99%
can you explain a little more how to use it?? or refer me some videos
Sir Keep it up,eagerly waiting for next video.
Thank You...😊.
You are an absolutely amazing teacher! Thank you for the great content
God damn man you're amazing, every doubt that I had during my education degree you solved it. Keep up the good work man :D
Dear Sir... thanks a lot for your clever and robust teaching ... please can i get presentation of this course ...
I become fan of you as your style of explaining the things. You explained the complex topics in such a lucid manner that audience could not be distracted for a single while.
Thanks Dr Manish 😊👍
have you understood clearly?
Its a great presentation !!! I appreciate your teaching method.
Brilliant! This was great. Thank you.
you are simply superb sir....
thankyou so much for this series.....
I have been following you from the ML Playlist , amazing content !! Thank you so much sir !
your videos are very helpful. It clears all doubts about machine learning and deep learning. can you please make a video on Deep learning using PyTorch?
You're god for the Beginner !!! The way you explain is way different than anyone else... SUPER AMAZING... HIGHLY RECOMMENDED
👍😊
Thank you so much sir, for this series
Sir your way of explanation is amazing Iam benefited from this your videos are good we appreciate your patience and way of explanation is beautiful.
Hi dhaval,
You have explained the concept in very simple manner. Could you please share all ppts of deep learning series? It will be a great help for all learners.
it needs time to develop, so wait for the time
very clear explanation.Your way of making complex things look simple and elucid serves as role model for each of us to be. Thank you so much sir
Glad it was helpful!
Hello Sir, this is amazing code walk through with very very good explanation.
Your videos are fantastic, I really appreciate the time you have taken to explain complex concepts so simply. Thank you very much 🙏🏼
Excellent video Great Explanation. very grateful for this
Sir in this video your practical example is different then our use case insurance. Appreciate you r effort
Thank you so much for making such a simple explanation. I cleared all my doubts that I had during my Education degree.
Glad it was helpful!
Very well planned and simply explained tutorial.
But I have a question, I have read that for multi-class classification problem, softmax activation is good, why did you use sigmoid activation here? Thanks.
Hi Sir, your videos are so insightful and well managed.
Thanks for sharing this.
Glad you liked it Mayank
Explain in very efficient manner. Very Impressive
really i have tried so many tutorials from today morning but this is like butter smooth thank you dhaval for such a great lectures and with all this knowledge you have a good hair with very little of this i lost 40%... JK really awsome thank you very much.
greatest teaching style. every detail comes in right position with actual meaning
Rauf I am glad you liked it
Great learning. Can’t thank you enough….great job
you are the best teacher in data science
One of the best tutorials.
Hi sir...first of all thank you alot for these videos..these videos are really amazing. Sir i have a project on radial basis neural network..if possible can you make a video for that?
Nice to see lot of good stuff on youtube. Wondering why can't everyone explain the way you explain. It seems I have finally landed at right place. By the way, how that pic become 7x 7 grid? Same for all image or is there any math behind it?
you are the best tutor for Deep Learning
Even though you have mentioned 3Blue1brown over here you have explained Neural Network with much concrete example here. Your examples are very easy to visualise and understand for beginners and in some cases it's even better than the great AndrewNG himself. Being from the field of education I see all the qualities of a great teacher in you, keep up the good work and thank you for such a good tutorial for free.
🙏🙏🙏 thanks and yes I am continuing it and even left my 9 to 6 job to do this full time
Thanks so much for great lecture you have made
Wonderful tutorials. Learning a lot! :)
You explain all of us like we are 5 yr old. This is very hard recipe to master. Thank you for everything. Please do not delete this videos from youtube ever. Stay healthy and stay happy. Thoda weight badao yaar. And please make a video with your manager which can guide us about inter company tarnsfers. Their are many who do not have finances to study in the US but we want to experience working their. This is my humble request. Thank u for ur videos
Thanks for the video , Keep up the good work , you are amazing , wish you all the best
It is always a pleasure to see a comment from you Fahad 😀👍
I wish I started to learn neural network with your tutorials. thank you
You the best. Thanks a lot for this nice video
This is mind blowing!
learning so much from you. Thank you for this series
Glad to hear it!
You are the best teacher❤️
i got the accuracy of 98.37 on training data and 97.32 on the test data and on the graph of truth vs predicted (tha we made using sea born library) the highest value other than diagonals was 19
Great video indeed! Love and Respect
Hey Dhaval, you made DataScience easy for me by all these wonderful playlist!! Thank you so much for all your efforts. Got accuracy: 0.1059 for loss = 'mean_squared_error'.
MSE is used for Regression problems not for Classification
@@slayer_dan don't be that guy
@@TheFadime123 sorry, i didn't get u
thank u so much....very helpful
thank you sir for all these lectures..
Very very good explanations !! can follow very good...
thanks alot for great explanation
Nice video sir, can you please put one full dedicated video on mathematical calculations on , lenier regression, decission trees,svm,sigmoid.......
Amazing!
Great explanation...
Thanks alot sir for this explanation
wonderful video. thank u so much
Hi,
For prediction of single image, you need to reshape to (1,784) i.e. X_test_flattened[0].reshape(1, 784).
i always get the score 0 could you explain that please
very good teacher, you are the man.
You got next level
Crisp, Clear, Uncluttered. I have a couple of online certificates (AI from Stanford and IBM data scientist professional). I have been looking for stuff on neural networks coding using Tensorflow. I tried a few lectures on youtube and left less than halfway through. This looks promising. Thanks Dhaval . I hope to complete the series and be able to do DNN stuff on my own the way I have been able to tackle other machine learning algorithms. Cheers
Bapai, thanks for the comment and I wish you all the best 😌👍
Your videos is a gold mine of knowledge.
😊👍
This is gold, thank you so much ❤️
Glad it helped!
Excellent
This video is awesome. I got 99.3 % accuracy when I changed epochs to 10 and out of curiosity I tried it for 20, accuracy is 99.8 now, finally for epochs = 30 accuracy is 99.94. I will try changing other parameters too.
Good job Jatin, that’s a pretty good score. Thanks for working on the exercise
are u trying some sort of prameter tuning ?
really helpful.
much appreciated, you're a great teacher
Glad it was helpful!
Nice vid!
thank you for wonderfull gift. i'm a NOOB programmer just starting to pythoning again.
to predict single data training input shape must be (,784)
you can either .reshape(-1,784) it or
X_test_flattern[[0]]
Thank you so much!
Sir, Create a video on hyperparameter tuning in deep learning like layers, activation function,etc.
Yet another awsome tutorial sir.Waiting for more out of this playlist.And i have a doubt,If i have my own hand written digit on a paper then how can test with this ? should i crop the image and predict it ? thank you
Yes you need the crop the image and then predict
Thank you very much for a nice video, but to be honest it seems very complicated for me as used image analysis.
I really want to input the variables number data for analyzing neural network, From this data I am really not able to apply my data in the same way of the images, Do you have any recommendation for me? Or you will have some more projects to work on the variables?
Thank you very much in advance for your kind answer
you have an amazing cadence to your speech.
great work thank you
Great video
Hello sir, thanks for beautiful explanation and I just got one doubt, why haven't you used standard scaler or Minmax kind instead of scaling it down by dividing 255?
He used min max scaling only. Notice that x_min = 0 and x_max = 255. Just omit the 0s.
scaled_x = (x - 0)/(255 - 0) = x/255
Great work! You are doing very good job.
I am happy this was helpful to you.
Thanks for this video, your tutorial is very clear
Glad it was helpful!
Thank you, Sir