Deep Learning using Matlab

[@mohamedabdallah400]

You are one of the greatest explainer of deep learning, thank you very much, gazakallahokhiran, from egypt

[@merlissoto3923]

Like and thank you very much for these tutorials!!! I have just finished this exercise and my neural network works perfectly... Thamk you! First of these three sections over.

[@zayyanushuaibu6188]

You are truly a genius, May God increase you in knowledge

[@ZakWlak]

Let's explain in previous videos that the whole idea behind deep learning is the back propagation algorithm. So in this video where the viewers are expecting an explanation of the back propagation algorithm, let's write a bunch of lines and tell the viewers "I think you understood that this is the back propagation algorithm". Bravo.

[@AliAhmed-f9o1m]

Great. you are a high level lecturer. I have got more benefits from this video. Many thanks.

[@zuzanna6385]

Thank you! You saved my life and project! Big love!!!!!

[@ahedalbadin2889]

Sir. You Are Amazing. Thank you very much. I know the NN theoretically but recently I needed to understand the Deep Learning process in few words.You've made my day. One Note: could you test the algorithm with not a perfect input and show the error.

[@NicoleQuimper]

thank you for these tutorials! the visuals are super helpful, there no way i could understand this without them you have an awesome teching method!!!

[@mursalinkabir]

A solid guide to deep learning

[@NuruzzamanFaruquis]

Thank you

[@vamsiakula653]

@@NuruzzamanFaruquis Why 20 nodes in the hidden layer?

[@ImUmar07]

Bests of the best. Great video

[@NuruzzamanFaruquis]

Thank you Erum Ashraf.

[@guido.demedici]

Congratulations to your really well made videos

[@abdullahalmazrouei9044]

For W1=2*rand(20, 25)-1 i think it should be W1=2*rand(25,20)-1. According to your source code the first parameter i.e. 25 represent the number of the previous layer, whereas the second parameter i.e, represents the number of nodes related to the next layer.

[@skalar-haubitze1619]

It is correct since it is exactly the other way around: the first number represents the number of the next layer, whereas the second parameter represents the number of the previous layer. Also I believe that if you would switch these two parameters by mistake you would run into an error because of wrong matrix dimensions.

[@ibrahimhaddadi2812]

this is a great point to start . thank you so much ,

[@NuruzzamanFaruquis]

You are welcome. Feel free to ask if you face any problem.

[@kanwalnaz2305]

Excellent work thanks

[@ahmadkelixo7243]

permission to learn sir. thanks you. from Indonesia

[@irfanuddin1948]

indeed a good tutorial to have an idea about deep learning

[@NuruzzamanFaruquis]

Thank you

[@3dp4e1]

Hi! Could you put a video about preparing a datastore for a multiple-input image classification CNN in Matlab? Thanks

[@abdullahullash8471]

very informative. looking forward for more videos.

[@NuruzzamanFaruquis]

Thank you. I hope this lesson will help you to understand the concept of DNN and train DNN easily.

[@arcimran5553]

Knowledgable....& informative topics..... carry on.

[@NuruzzamanFaruquis]

Thank you

[@vamsiakula653]

Why are they 20 nodes in the hidden layer? Please answer

[@sohaibshaikh5534]

25-5=20

[@muhibullah2134]

thanks for sharing your valuable lectures

[@xdgamestudios2660]

It's me from the other video, Thank you soo much for you tutorials and explanations, I think I'm starting to understand how I should do it!! one other thing... in the case I'm working I have 2000 input nodes and only a binary output... 1 or 0 how should the correct output be?! only one value?! I don't know I can I make it end with one single node!

[@NuruzzamanFaruquis]

Take two output nodes - [1 0] for true and [0 1] for false. Otherwise even for no signal, there will be confusing false result.

[@imrankanj00]

Your contents are well arranged. I have couple of questions. 1) what is the logic of exponential and division in softmax function? 2) In my case I have an image of 20x30 with output male/female, how many input and output nodes, I need?

[@NuruzzamanFaruquis]

Thank you. You asked a good question. 1) please plot the output of 'softmax' function separately. It will answer your question. It is difficult it express it properly using text. 2) for every pixel, there will be one input nodes. For 20*30 image, you need 600 input nodes. 3) There are two classes - (i) male and (ii) female. So two output nodes.

[@imrankanj00]

@@NuruzzamanFaruquis Thank you for your reply. Now I get it. Is it possible, you suggest some book/material where I can get a detailed and more complex example. I know this question is not related to this video but if you can help, I will appreciate. Thank you.

[@ashik2052]

Informative Carry on genius Waiting for ur nxt Video

[@NuruzzamanFaruquis]

Thank you

[@harikrishnaponnam4680]

Thank you very much it's very helpful for me.

[@raghad3189]

Thank you very much , but i have quastion when i write save ('neoral network.mat') and load('neoral network.mat') Matlap saing this error !why??

[@kamgangblaise2645]

Thank you for the video. I have a question. What if your inputs and output to the network are sensors data, how can it be done because i am having some troubles

[@NuruzzamanFaruquis]

You need a data processing module in between the sensor node and the input nodes of the network. That's all you need to do.

[@kamgangblaise2645]

@@NuruzzamanFaruquis Data processing module?

[@kamgangblaise2645]

@@NuruzzamanFaruquis I have 3 inputs data (1 reference and 2 sensor data) and 1 output data

[@AndreaBarucci80]

Thank you vert much, well done and very useful! I hope you will insert other videos about Deep Learning and Machine Learning using Matlab.

[@merlissoto3923]

Teacher, there is something I don´t understand. In the section of adjustment at the DeepLearning function: adjustment_of_w4 = alpha*delta*output_of_hidden_layer3'; adjustment_of_w3 = alpha*delta3*output_of_hidden_layer2'; adjustment_of_w2 = alpha*delta2*output_of_hidden_layer1'; adjustment_of_w1 = alpha*delta1*reshaped_input_Image'; alpha is a real number, delta is a (5x5) matrix multiplied for (output_of_hidden_layer3') wich is a (1x20) matrix... I don´t get this part And similar for the rest: delta 3 is a (20x5) matrix, and (output_of_hidden_layer2') is a (1x20) matrix delta 2 is a (20x5) matrix, and (output_of_hidden_layer1') is a (1x20) matrix delta1 is a (20x5) matrix, and (reshaped_input_Image';) is a (1x25) matrix How are this multiplication among matrix possible? and I know it works perfectly because I did the exercise Hope you might read and answer this message. Thanks in advance! Atte, Merlis.-

[@عبدالوهابفوزى-ث8ط]

Thank you for these tutorials!

[@kavaskarsekar2835]

Hi , I understand the DL concept but have a question that how to choose hidden layers and hidden neurons. Is there any specific rule or trial and error?

[@jeancarlosadrianza7416]

Hi, Nuruzzaman. Your video "Neural Network using Matlab" doesn't show its comments (at least in my PC). For this reason, I want to congratulate you for that great video. Your explanations are very clear. I wanted to create a more general version of your SGD_method() to solve a exercise that I must to do, however, when I arrived to the calculation of delta and deltaweight, I realized that both calculations would produce incompatible multiplication of matrices. Please let me show you: delta = output * (1 - output) * error; If I treat output like a array, I don't know the procedure to calculate delta. In my opinion it's impossible, because if (for instance) output has a order 2x1, (1 - output) also would have this order. Then we will have a multiplication of 2x1 and 2x1... and then we have the array error. Something similar happens with deltaWeight. I would like to know if you can make a video to show how to handle the training and testing of a perceptron with more than one output. My bigger doubt is: how is affected the matrix of weights when a error appears in (for instance) the output 1? The error can affect every element of the matrix or only the weights related with that output? Best regards and congratulations again!

[@themostimportantwatchtowat8943]

Thanks. It's a really great tutorial

[@KraiemRayene]

Hi, thank you for this video. can you send me the datasets your used for this application

[@snehalgaikwad6436]

Plz take lecture on hopfield neural network to train the image

[@NuruzzamanFaruquis]

I will try.

[@Elmusrati_Channel]

Thank you for the well prepared video. But this is just the conventional backpropagation algorithm known since 30 years. I did the same using basic language more 23 years ago. It would be better if you show how to use deep learning ready functions in Matlab. No one need to write the details of coding nowadays. Thank you

[@NuruzzamanFaruquis]

I agree with you. With such experience you do not need the details of coding. However, I have some enthusiastic new learners around. They want to learn from the very basics. Thank you for your valuable suggestion.

[@zeyadalabsy7332]

@@NuruzzamanFaruquis I have a question, why do the weights become NAN if we set 1 to more than one value in the outputs? for example if the first output is [0 1 1 0 0], the weights become NAN. Could you please explain it?

[@kumudaranimahanta7634]

can you make a video explaining all the basic things like nnet, epoch, newp etc and code of training neural network in matlab

[@neelabhjyotisaharia1979]

Was helpful...One question: How to back propagate when there are also biases with the weights?

[@MuhammedAliErbir]

teşekkür ederim kardeşim, anlaşılır şekilde anlatmışsın, eline sağlık Kırıkkale Üniv. Bilgisayar Müh. Doktora Talebesi Muhammed Ali ERBIR

[@basharsaad4142]

i'm in cukurova university ,Turkey , ADANA are you working on image processing like object detection

[@ahmadalghooneh2105]

May the elder gods bless you with even more knowledge, thank you thnk you thnk you

[@raihanrafique7406]

Great Presentation,

[@123nops]

This video is very helpful. I am doing a cryptanalysis on my research right now using deep neural network. May I ask if I will use this code on your MATLAB demo, on which part should I define my 128 input nodes and 128 output nodes? Yours has 25 input and 5 output nodes? Thanks for the help.

[@abdullahalmazrouei9044]

Goodmorning sir, Why you used exactly 20 nodes for hidden layers? I did research on books but i hzvennt understand how to choose the correct number of nodes for hidden layers. Kind regards Doctor.

[@durgasreeraghavan2049]

I am chemical engineer, I am new to this machine learning programs. My thesis involves ANN. I have the same question as natalia chu has. How do I decide no. of neurons in hidden layer?

[@NuruzzamanFaruquis]

I am still studying about it. I will share my findings in upcoming lessons.

[@fatihaji5339]

Hi, the video was very good. To set up the network, my input data is 1000 x 3 and my target data is 1000 x 1. The output of training and testing their values is more nan. Why is this happening? What are the reasons for this?

[@willdonell8508]

Hello, I have one question. Is your delta corresponding to the generalized delta rule? I can’t find the derivative of ReLU function Good tutorial!! It’s very helpful for me👍 Thank you very much for sharing

[@AkashSingh-rv8vx]

Derivative is 1 in non negative region. Elsewhere zero. Not defined at zero though.

[@nuttaponkasemsuk7670]

Did you use sigmiod to calculate the weight as taught in the clip first?

[@BappaMukherjeeismdhanbad]

can you please make a video on how to fix the optimum number of layer and what should be the neurons in these layers

[@know_edu1406]

Fine, your explanation. Can you tell in which ways (CNN or, Sparse Autoencoders or RBMs or LSTM are used to implement this Deep Learning?

[@supriyanaik316]

Can I give any 2d cad geometry data as an input to deeplearning in matlab??

[@NuruzzamanFaruquis]

Yes, you can. You have to process your data accordingly.

[@supriyanaik316]

@@NuruzzamanFaruquis can you give some ideas that how to start it?? Should I convert all 2d cad geometry into image file or what???

[@tonikpinik7392]

Really it's amazing...

[@nataliachu2495]

Thank You for lecture! I am still puzzled - why exactly 20 neurons in hidden layer? Could You, please, provide formula for calculation?

[@rajasekarsuriya6269]

Bro,y he is using only 20 nodes in hidden layer.Y is it so??

[@mathi_s]

@@rajasekarsuriya6269 Hi, did you got the answer for how to optimize the number of neurons.

[@AkashSingh-rv8vx]

Hello Nuruzzaman Sir, can you explain how you arrived at error of hidden layers as the product of next weight and delta of next node? Please share the source if you are short of time.

[@chetansoni8620]

sir can i use this same code.. instead of our digit (matrix) can we convert an RGB image to binary - then with binary matrix suppose the already known output i will give which is same as in this video... If i do this then how much epoch will i need if i will give 20 images of 28*28 pixels..?? please answer sir

[@ofijanabu4689]

thank you very much Ifound it as best lecture would you please tell me how to realize a communication system on deep learning?

[@cherylli4769]

Very helpful, thank you!

[@lama8oct]

I wrote the code like you did in the video but i had error, how can i solve it??

[@hakanbaki509]

hello, i have a question i have implemented the neccessary argumets to create XOR funcion with the code i have learned just by now. however when i apply [0;0] as it descibes the very first rule of XOR gate, i am getting 0.2500 in each segments while it must give me apprximately 1 in the first input. why i am facing that problem? edit: well i might saw the problem. just because all the inputs are 0 at the first rule of gates, none of this gate implementations will fit to your code due to the hidden layer values will be zero(for all output it is getting the equal sign which is 0.25). so i need to think about something else to implement gate configuration of ANN.

[@merveozdas1193]

Thank you so much for your sharings :) 👏👏 but I have a question, if I use correct_output(:,:,k)', is it true or if I use input_image(k,:)', is it possible? I couldn't understand the matrices what you mean

[@Akshay-cl5mz]

Sir, can i use this method for estimation of heat for my batch reactor temperature control ? Please respond

[@ferdaozdemir]

Hello Again, I ran the example and updated it for a larger input set. The updated version works but the softmax function returns Nan after a few samples during training which affects the whole training session. I searched for a safe version of softmax but couldn't find one. I found one example with phyton np. I install phyton and implemented the function but getting errors such as "Conversion of MATLAB 'sym' to Python is not supported". I would be appreciated if you can suggest a safe softmax function implementation?

[@chetansoni2795]

The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer

[@AmmarAhmedSiddiqui]

what if we feeed an input image which is not of "1" but close to "1" ?

[@SABIRsabir-tn2oj]

thank you its very good idea

[@dragonex68]

thanks for the videos. this is useful during lockdown haha

[@NuruzzamanFaruquis]

Glad you liked it!

[@vamsiakula653]

@@NuruzzamanFaruquis Why are they 20 nodes in the hidden layer? Please answer

[@jonathancohen64]

How can I work out which input is the most effective / had the largest meaning to the output of a neural network

[@niranjanshegokarniranjanshegok]

Thanks for nice tutorial. Please provide the ReLU.m file.

[@dr.b.srinivas5505]

Excellent.

[@NuruzzamanFaruquis]

Thank you

[@ahmedbaba3715]

Thanks for the explanation ... Please, I want a code (neural networks) in order to check the quality of water ..... and God bless you good

[@meghnatripathi5571]

have you written the code . i need help

[@md.ashikurrahmankhan582]

Thank you for you nice presentation. However, during running the code with Matlab2016a I found error as follow function [w1, w2, w3, w4] = DeepLearning(w1, w2, w3, w4, input_Image, correct_Output) ↑ Error: Function definitions are not permitted in this context. function y = ReLU(x) ↑ Error: Function definitions are not permitted in this context. Undefined function or variable 'ReLU'. >> TrainingNetwork Undefined function or variable 'ReLU'. Error in DeepLearning (line 9) output_of_hidden_layer1 = ReLU(input_of_hidden_layer1); Error in TrainingNetwork (line 46) [w1, w2, w3, w4] = DeepLearning(w1, w2, w3, w4, input_Image, correct_Output); Could you suggest where is the problem! Thanks in advance.

[@NuruzzamanFaruquis]

I have rechecked the code. It is working properly in my end. Would you please switch to MATLAB 2018B or layer version and try again?

[@МихайлоДимянчук]

Why you used exactly 20 nodes for hidden layers?

[@LYu-rw5ze]

very helpful, thank u!!

[@NuruzzamanFaruquis]

You are welcome :)

[@shihabo3]

well organized video

[@amelbenzaid7440]

why 20 nodes??????????????????

[@Tribeball359]

Thank you so much for your lecture! Do you have any example on how to do semantic segmentation of an object in an image? And what if the input image is not a grayscale or binary? For example RGB or RGBD image?

[@NuruzzamanFaruquis]

Right now I do not have any example on semantic segmentation. However, I will try to prepare one for you. And and RGB image has three different values for single pixel. The pixel values are the input signal. We cannot take three input signal at the same time in a single input node. That's why we use grayscale image.

[@Tribeball359]

@@NuruzzamanFaruquis Thanks a lot! So, for multi channel input we need to modify the DCNN structure...

[@meghnatripathi5571]

@@NuruzzamanFaruquis sir can you please help me on a code to find wear rate for my project . i am really stuck???

[@tnpscmaterial627]

Sir, how to recognition images in video scene using deep learning in matlab

[@basharsaad4142]

Thank you sir can i do this code for gray image ? ???

[@username42]

any chances to get the codes and the files from the video ? btw, we dont test our model with the same data we trained, it does not make sense, it is totally biased. you should have been created another data set for testing. and what about the validation of the model you have been trained. so here u have just only train the model with just 5 input without any validaiton and then use the same 5 inputs as showing testing which is not at all neither can be also as validation.

[@tkhankhoje39]

can u upload for simple inputs and output values with normalization of variables?

[@foodsscenes5891]

Thanks for the useful instructions; I have a project which needs your help. How can I contact you?

[@qusayhamad5741]

thank you good work

[@zainabhaddad3390]

hi , I need a help for preparing an RCNN multiclasse (multi object detection ) code in matlab can you help me please

[@Profeamgad]

Thanx How can I obtain equation model from nn-tool matalb

[@ImUmar07]

Can you explain neural networks algorithm with machine learning in prediction of anything

[@NuruzzamanFaruquis]

Are you asking about predictive model using neural network?

[@ImUmar07]

@@NuruzzamanFaruquis yes. In Matlab plz with code also. I'm beginner in learning all these concepts.

[@NuruzzamanFaruquis]

@@ImUmar07 I understand. I will try my best to prepare a tutorial that will help you building solid understanding.

[@valerianherzog6597]

Thank you for this very helpful videos! Could you explain how you get the number of neurons in the hidden layers? Is there a specific rule?

[@haram812]

adjustment_of_w4 = alpha * delta * output_of_hidden_layer3; all steps are correct but i have dimension error here

[@meghnatripathi5571]

why are there 20 hidden layers ?

[@dr.avijendar5631]

sir..can you provide code for image classification using DNN

[@NuruzzamanFaruquis]

Sure I can. Would you please tell me which classification (binary or multi-class) you are talking about?

[@dr.avijendar5631]

@@NuruzzamanFaruquis multi class mr image classification i need

[@user-nf7iu2qg4b]

Hello sir .. iam Appling phd .. I need your help please

[@open_game_box944]

Why 20?

[@agungsusilo530]

thank you so much sir, i tried exactly the sam ecode but it says that "Error using - Matrix dimensions must agree." can you help me with this problem? thank you in advance

[@anushkabhatt1129]

I have a numeric matrix as input file for training. Can I train that using deep learning

[@NuruzzamanFaruquis]

Yes of course. Why not?

[@البداية-ذ1ذ]

I have question please my laptop is cpu and i want to ran this code with data consists of arround around million samples ,can i do that

[@NuruzzamanFaruquis]

Yes. You can do it. However, the training process will take longer.

[@darmatv9882]

Hi! I truly enjoy your video. I even tried to run the same code in MathLab R2016a, but it is given me an error message "Not enough input arguments reshaped_input_image = reshaped(input_image(:, :, k), 25, 1); " I want to use this method for face recognition, because I am conducting research on a topic Improving the effectiveness of Deep Learning Approach for Face Recognition . How can I see the designed Architecture of this Algorithm in the Matlab that is the Network Architectural design and how to input face images into the network?

[@zayyanushuaibu6188]

you dont have to run this file that shows error, run the the test file straight it will work. run=deepnet then testdeepnet

[@preetikatiyar27]

sir please provide source of medical imaging

[@RahulSharma-xo5nd]

Please make a video on medical image i.e. .nii file

[@ferdaozdemir]

Thank you for the wonderful explanation. I just couldn't understand why we explicitly work in hidden layers in TestDeepLearning.m. Didn't we already trained the network and saved it. Why do we deal with inner layers of the network at this step. I would expect that using the "DeepNeuralNetwork.mat" would be sufficient to test? Can you please provide some explanation for this part ..

[@Maha-mo5sf]

your input image number 2 and 3 is the same

[@jawadur_]

useful!

[@NuruzzamanFaruquis]

Thank you

[@rordic.y5947]

Did anyone take the homework of "why 20 notes"?

[@vamsiakula653]

Same question from my side? What is the answer?

[@AkashSingh-rv8vx]

I think one can take any number of nodes from structural point of view which will only effect the number of weights. However, this selection will impact the performance of model.

[@SA-pg5jt]

thanks