Machine Learning / Deep Learning Tutorials for Programmers playlist: ua-cam.com/play/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU.html Keras Machine Learning / Deep Learning Tutorial playlist: ua-cam.com/play/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL.html
Dear Mandy you are wonderful, you are a real teacher that I' have seen in my life your videos are really super informative you can not imagine how much your videos helped me God bless you you will be in my mind forever, thanks a lot
You are amazing I must say. You not only share the best tutorials but also answer people's questions and that too with great detail. Keep up the good work!
It is clear that the image "NN.PNG" was loaded into the program and displayed, however is there a way to generate a neural network architecture diagram based upon the code that implements it?
i was also search for the same thing on the internet but i think now there must be any possibility to draw the same graph of your code if there is any and any one know kindly let me know in the comment
Thank you, Prabu! The full series is here: deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU Many other NN series are on the website as well :)
{ "question": "How do you pass in layers into a sequential keras model?", "choices": [ "as an array", "as a list of function arguments", "as an object literal", "it isn't possible" ], "answer": "as an array", "creator": "Hivemind", "creationDate": "2020-10-20T22:01:48.221Z" }
Thanks, Géza! Just added your question to deeplizard.com/learn/video/FK77zZxaBoI :) Note that I modified it to have an answer "as a list" and updated the written portion of the blog to reflect this, as technically the data type of what the Sequential model expects is a list, not an array. Thank you!
Great videos! Question: Is the output layer an ACTUAL output like "Cat" and "Dog"? Or is the output layer technically another hidden layer but it just happens to be the last hidden layer so it's the one that determines the classification decision? Thanks for clarification!!
The output is a probability distribution among possible classes. The class with the highest probability is the one that we choose as the predicted label from the network. This point will be clarified further as you progress through the course.
@@deeplizard hmm ok I am looking forward to the rest of the course! I guess I am having trouble distinguishing the difference between an output layer and an output. Hopefully that will clear up. Thanks so much for the response. Liked and subscribed. All the best
In the NN.PNG graph, why doesn't the first neuron at the hidden layer connect to the second neuron at the output layer? Is it a misdrawn or intentional? Thank you.
Thank you, Owais! I have Tensorflow on my list to explore as a possible topic/playlist for the future. For now, I have a Keras playlist that focuses on the coding-side of neural networks in case you haven't seen it yet. ua-cam.com/play/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL.html
Thanks, Yu! In later videos, I zoom in on the code :) Also, you can check out the corresponding blogs as well to get a better view of the text: deeplizard.com/learn/video/FK77zZxaBoI
from keras.models import Sequential from keras.layers import Dense, Activation model= Sequential([ Dense(7, input_shape=(10,) activation='relu'),Dense(2, activation='softmax'),] ) the last line is giving syntax error please help
hi deeplizard I'm following this tutorial series,i love this but i try to genarate same layers graph using scipy it says module 'scipy.ndimage' has no attribute 'imread' any solution
node output = activation(weighted sum of inputs) => this also need the 'bias' term right? So the actually it would be node output = activation(weighted sum of inputs+bias)
is it bad to have many hidden layers or even few?? What if I have 362 data or inputs but i only have 128 hidden layer? is it bad? Or what if I only have 20 classification but I have 50 nodes in output layer?
Shouldn't each neuron in the hidden layer be connected to each neuron in the output layer? If so, the first neuron of the hidden layer may be missing a connection to the second neuron of the output layer. (...and thanks for these didactically excellent videos!)
{ "question": "A node's output depends on:", "choices": [ "The weighted sum of inputs", "The product of inputs", "The sum of weighted connections", "The sum of inputs" ], "answer": "The weighted sum of inputs", "creator": "Chris", "creationDate": "2019-12-07T01:42:10.934Z" }
please clarify my doubts mam, if the assigned weights changes for optimization then it will not get confuse?? and my 2nd doubt is when ur making dense layers in coding frist u used relu and then u used softmax?? why so
Thank you, awesome tutorial. But, I have a doubt about multi-class problem, is it possible to have a model having 11 unit(features) input layer and 5 unit(classes) output layer with no hidden layer.
Hi, I tried plotting the neural network using the matplotlib code you used but I seem to be getting an attribute error"module 'scipy.ndimage' has no attribute 'imread'
Great explanation mam! Suppose I have 3 features, each feature has 4 possible values. Meaning they are not only confined between either 0 or 1. Is that okay for Neural Nets?
Thanks, Md.Yasin Arafat Yen! Yes, having labels that aren't 0s and 1s is fine. Most of the time though, you will standardize the labels. You can see more about this in this video on preprocessing data for training a neural network: ua-cam.com/video/UkzhouEk6uY/v-deo.html And I also discuss another labeling technique called one-hot encoding in this video: ua-cam.com/video/v_4KWmkwmsU/v-deo.html
Thanks! I meant my features are not binary! Suppose I have 3 features and 1 Class: A B C Class 1 1 2 1 2 0 1 0 0 2 0 2 Here I don't need any encoding right?! Does ANN works with this type of data?
Hey! for some reason, the activation functions dont work for me. It states when hovering over 'Activation' : "unused import statement" and says that "relu" is a typo. What should I do? Thx in advanced !
hi deeplizard, when i execute the code mentioned above in jupyter, it says 'FileNotFoundError: [Errno 2] No such file or directory: 'NN.PNG'.....can you please help. thanks in advance
Thanks, Minhaaj - We're working on getting this course updated 😄 In the mean time, the Keras course has been updated with the latest code (now fully integrated with TensorFlow) at the link below. deeplizard.com/learn/playlist/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL I'd imagine the code you posted here isn't working perhaps due to not importing the Keras library from TensorFlow (new way now that it's integrated), as the underlying syntax for the Sequential model remains the same in the new Keras versions. (I also notice that your "s" in Sequential is not capitalized, but not sure if that was just a typo from writing into the comments.)
When you explained input_shape I got a little confused. Did you mean that input_shape=(3 ,) means that you have 3 input neurons or did you mean that each neuron in the hidden layer will receive 3 signals each? Or maybe that's the same thing?
Hey Christoffer - 3 input neurons. For example, if each sample in our training set was an array containing a person's age, height, and weight, then the network would have 3 input neurons. Each neuron represents a single feature from the sample. One for age, one for height, and one for weight.
Thank you! As a follow up question if you had for example 20 images of numbers (0-19) in this type of network would you only assign 1 neuron because all would be classified as numbers? I'm sure you would use another more effective technique for that but just so I understand correctly or maybe it doesn't work at all for that. I'm very new to deep learning so sorry if the question is confusing.
For images, each individual pixel is considered a feature. So, if you had a grayscale image that was 20x20 pixels in size, then in total you would have 20 x 20 = 400 total inputs to the network representing a single image. You might be interested in the videos/blogs below where we expand on this topic further when we cover convolutional neural networks (CNNs). deeplizard.com/learn/video/k6ZF1TSniYk deeplizard.com/learn/video/gmBfb6LNnZs If you need a basic explanation of CNNs before jumping into those, then check this one out first: deeplizard.com/learn/video/YRhxdVk_sIs
Thanks for another super informative video. I have one question: how are the weights of the connections between input- and hidden layers being determined? Thank you for your time and shared knowledge :)
Hey Dragana - The weights are first randomly initialized, and then they are optimized during the training process with Stochastic Gradient Descent (SGD). SGD makes use of backpropagation to find the optimized weights. Don't worry if you're not yet familiar with these concepts. The videos below, which come later in this playlist, explain these topics in detail. Weight initialization: ua-cam.com/video/8krd5qKVw-Q/v-deo.html Backpropagation (5 part series): 1. ua-cam.com/video/XE3krf3CQls/v-deo.html 2. ua-cam.com/video/2mSysRx-1c0/v-deo.html 3. ua-cam.com/video/G5b4jRBKNxw/v-deo.html 4. ua-cam.com/video/Zr5viAZGndE/v-deo.html 5. ua-cam.com/video/xClK__CqZnQ/v-deo.html Show less
nn.png is just an image of a neural network that I placed in the same directory as my jupyter notebook to print the image of the network. It is not required for anything else, but you can get an image of a network, save, and print it as well if you prefer.
@@deeplizard thanks for the quick response. Now I can figure out the issue. As I'm using google colab, which is an online platform, so I'm facing the problem of directory. Can u kindly help me fix the issue? If your aren't busy, u can try the same code in google colab. Thanks.
I begin zooming in on the code in later videos :) Additionally, you can use the corresponding blog for each video at deeplizard.com. The text is easy to read there.
It worth noting that the information of this course is well structured and very clear. I can recommend this course to anyone new to machine learning. Thanks a lot for this course!
Hey @deeplizard, Thank you so much for answering my question that even ChatGPT couldn't answer it clearly. I am actually a beginner in deep learning. Your explanation has answered my question. In fact, I have subscribed to your channel as well!
Probably a dumb question, but in your neural network image, aren't we supposed to have an edge between the top node in the hidden layer and the bottom node in the output layer ?
Hey Yohann - Not a dumb question at all! You're right, there should be an edge there. I didn't notice that missing detail! In later videos of the playlist, we start using another network as our visual, so this missing edge won't persist for too long.
{ "question": "__________________ are used in models that are doing work with time series data.", "choices": [ "Recurrent layers", "Normalization layers", "Recurent layers", "Pooling layers" ], "answer": "Recurrent layers", "creator": "Kanishk", "creationDate": "2022-11-13T06:06:16.798Z" }
Hi, I understand how layers work and everything, we give a number of inputs, do a few operations on layer to get a certain number of outputs in the end. But how do you know how many hidden neurons to put? I looked online and couldn't find any explanation other than "it depends on what 'rule' you're using". So I can put how many I want? ok but what is the best number to use then? I found another saying "well, you try with more or less neurons and you check by hand". Is there any better way to find out ? If you have an idea... :) Ty
@deeplizard..why did we used np.expand_dims and why we are plotting the img using index img[0]...i know it is a silly question to ask but i don't mind asking..
Hi there, before asking my question I wanna thank you for your amazing and well explained videos, my question is: how do we specify the number of neurons in the hidden layer ? Do we choose whatever number of neurons we want or is the number of the latter in the hidden layer tied to something (which I don't know) ?
You're welcome, Lightning Blade! Generally speaking, the more complex your data, the more layers you can expect to have in your model, and the more neurons in each layer you'll likely need. Also, you'll typically see that the number of neurons increases within each layer as the layers become deeper in the network. There is not a general rule of thumb that I'm aware of to follow for choosing how many neurons to include in each layer. It's more of mixing trial and error along with experience from what's worked in previous models.
Thank you very much for the explanation, I'll make sure to watch all of your deep learning videos, I might have some other questions in the coming videos which I hope it won't bother you. Thank you again.
Thank you, ibrahim! Glad you're enjoying the series! We have many other ML and DL series available here on UA-cam and on deeplizard.com. You can browse all available series on the deeplizard.com home page!
Machine Learning / Deep Learning Tutorials for Programmers playlist: ua-cam.com/play/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU.html
Keras Machine Learning / Deep Learning Tutorial playlist: ua-cam.com/play/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL.html
I like that you break out topics into short video sections. Thanks for doing that!
Glad to hear! Thanks for letting me know!
I would like that you take breaks >5ms inbetween sentences. :)
I have searched and even paid to some courses, but this is just the best! Your explanations are wonderful! God bless you!!
Dear Mandy
you are wonderful,
you are a real teacher that I' have seen in my life
your videos are really super informative
you can not imagine how much your videos helped me
God bless you
you will be in my mind forever,
thanks a lot
Your videos are infinitely better than anyone else. I'm might as well just search your channel instead of UA-cam for these topics.
Search the website also :D
deeplizard.com
I really thank you for making this free and available to everyone!
You are amazing I must say. You not only share the best tutorials but also answer people's questions and that too with great detail. Keep up the good work!
Thank you, Faeza!
This really helped me understand what I was missing. Thanks!
Thank you very much for this video! This is the first time I understood how Convolution is a kind of layer in an Artificial Neural Network!
Thanks!
Someone on Reddit recommended me this channel to get into Deep Learning.
Now I have a better understanding what the Neural Networks are.
This is so concise. Thank you for putting this together
This is best channel to learn from period
The* ffs cant type for shit
Thank you so much for the video!
Wonderful! I was searching alot about layers but i could'nt find, and here is what i was searching for thanks deeplizard!
It is clear that the image "NN.PNG" was loaded into the program and displayed, however is there a way to generate a neural network architecture diagram based upon the code that implements it?
i was also search for the same thing on the internet but i think now there must be any possibility to draw the same graph of your code if there is any and any one know kindly let me know in the comment
thank you a lot for your help
Thank you
Thank you so much, your explanation is very simple :) thank youu
good work, now I understood how NN in keras works,thanks for ur help.hope u do more videos like this.
Thank you, Prabu! The full series is here:
deeplizard.com/learn/playlist/PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU
Many other NN series are on the website as well :)
Very nice explaination.Thank u so much .
Incredible, I'm learning so much
Great video!
awesome vid guys! :)
{
"question": "How do you pass in layers into a sequential keras model?",
"choices": [
"as an array",
"as a list of function arguments",
"as an object literal",
"it isn't possible"
],
"answer": "as an array",
"creator": "Hivemind",
"creationDate": "2020-10-20T22:01:48.221Z"
}
Thanks, Géza! Just added your question to deeplizard.com/learn/video/FK77zZxaBoI :)
Note that I modified it to have an answer "as a list" and updated the written portion of the blog to reflect this, as technically the data type of what the Sequential model expects is a list, not an array. Thank you!
Thank you soo much.
Excellent content as usual man. love it
You are simply awesome.
great videos!!
Great videos! Question: Is the output layer an ACTUAL output like "Cat" and "Dog"? Or is the output layer technically another hidden layer but it just happens to be the last hidden layer so it's the one that determines the classification decision? Thanks for clarification!!
The output is a probability distribution among possible classes. The class with the highest probability is the one that we choose as the predicted label from the network. This point will be clarified further as you progress through the course.
@@deeplizard hmm ok I am looking forward to the rest of the course! I guess I am having trouble distinguishing the difference between an output layer and an output. Hopefully that will clear up. Thanks so much for the response. Liked and subscribed. All the best
In the NN.PNG graph, why doesn't the first neuron at the hidden layer connect to the second neuron at the output layer? Is it a misdrawn or intentional? Thank you.
This was not intentional, and I didn't catch it until after the upload. Good eye :)
In thefigure 2:52, there is a line between the hidden layer neuron 1 and output layer neural 2 missing. Thanks for explaining!
Good eye, you're right thanks!
thanks a lot for this video
Ty.
Please make a videos on NLP like word embeddings, LSTM, GRU etc
its a one of best tutorial about AI on youtube if it is possible for you then please make tutorial on tensorflow
Thank you, Owais! I have Tensorflow on my list to explore as a possible topic/playlist for the future. For now, I have a Keras playlist that focuses on the coding-side of neural networks in case you haven't seen it yet.
ua-cam.com/play/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL.html
Amazing video, very helpful for understanding these concepts. Just a little piece of advice, the font size should be larger :D.
Thanks, Yu! In later videos, I zoom in on the code :)
Also, you can check out the corresponding blogs as well to get a better view of the text:
deeplizard.com/learn/video/FK77zZxaBoI
@@deeplizard I'm reading the blog now, really they're very helpful
Perfect! Glad to hear that :)
Not gonna lie the whole playlist is helping me a lot and probably the best explanation on Ml and AI on youtube
Why aren't you gonna lie?
@@chaoukimachreki6422 Because he is a virtuous gentleman.
@@richi1235 XD hey Ex be nice , that was a genuine question.
My question is, My do you chose 5 neurons in the hidden layer? why not other numbers and does it matter?
So n in Dense(n) depends on how many things you are predicting? I tried to put n = 2 in last dense layer of cnn model but it gave me error. Why?
from keras.models import Sequential
from keras.layers import Dense, Activation
model= Sequential([ Dense(7, input_shape=(10,) activation='relu'),Dense(2, activation='softmax'),] )
the last line is giving syntax error
please help
what does the hiddon layer exactly hide?
Very helpful, thanks (y)
Actually>>>> you are the best ..... that's it .
hi deeplizard I'm following this tutorial series,i love this but i try to genarate same layers graph using scipy it says module 'scipy.ndimage' has no attribute 'imread' any solution
node output = activation(weighted sum of inputs) => this also need the 'bias' term right? So the actually it would be node output = activation(weighted sum of inputs+bias)
Yes, bias is covered in detail here:
deeplizard.com/learn/video/HetFihsXSys
is it bad to have many hidden layers or even few?? What if I have 362 data or inputs but i only have 128 hidden layer? is it bad? Or what if I only have 20 classification but I have 50 nodes in output layer?
Shouldn't each neuron in the hidden layer be connected to each neuron in the output layer? If so, the first neuron of the hidden layer may be missing a connection to the second neuron of the output layer. (...and thanks for these didactically excellent videos!)
You're right, I didn't notice the image was missing a connection during the recording 😅
{
"question": "A node's output depends on:",
"choices": [
"The weighted sum of inputs",
"The product of inputs",
"The sum of weighted connections",
"The sum of inputs"
],
"answer": "The weighted sum of inputs",
"creator": "Chris",
"creationDate": "2019-12-07T01:42:10.934Z"
}
Added to the site! Thank you :)
please clarify my doubts mam, if the assigned weights changes for optimization then it will not get confuse?? and my 2nd doubt is when ur making dense layers in coding frist u used relu and then u used softmax?? why so
The top node of the hidden layer is supposed to be connected to the bottom output node, right?
That's right. I didn't notice it was missing until much later.
what does it mean that a input-data is shaped into 3?
So would there also be weights from the hidden layer to the output?
yes
Thank you, awesome tutorial.
But, I have a doubt about multi-class problem, is it possible to have a model having 11 unit(features) input layer and 5 unit(classes) output layer with no hidden layer.
Hi, I tried plotting the neural network using the matplotlib code you used but I seem to be getting an attribute error"module 'scipy.ndimage' has no attribute 'imread'
Yes, even i got the same error in google colab and in jupyter it says FileNotFoundError: [Errno 2] No such file or directory: 'NN.PNG'
where can i find the code please?
Great explanation mam!
Suppose I have 3 features, each feature has 4 possible values. Meaning they are not only confined between either 0 or 1. Is that okay for Neural Nets?
Thanks, Md.Yasin Arafat Yen! Yes, having labels that aren't 0s and 1s is fine. Most of the time though, you will standardize the labels. You can see more about this in this video on preprocessing data for training a neural network: ua-cam.com/video/UkzhouEk6uY/v-deo.html
And I also discuss another labeling technique called one-hot encoding in this video: ua-cam.com/video/v_4KWmkwmsU/v-deo.html
Thanks!
I meant my features are not binary! Suppose I have 3 features and 1 Class:
A B C Class
1 1 2 1
2 0 1 0
0 2 0 2
Here I don't need any encoding right?! Does ANN works with this type of data?
Yes, that's completely fine!
merci
Hey! for some reason, the activation functions dont work for me. It states when hovering over 'Activation' : "unused import statement" and says that "relu" is a typo. What should I do? Thx in advanced !
Modelling Neural Network 5 input and 8 hidden layers and further 5 hidden layers and 1 output plx answer me?
please tell me how to identify the number of hidden layers for a model,tell me if there`s any mathematics behind it!
Great video;
Is it possible to have an artificial neural network of multiple-input and just a single output?
Thank you.
Yes. For example, regression can be modeled using a neural network.
@@deeplizard Thank you very much. Is there a way I can rescale the ANN regression output to percentage (0 to 100)
Nicee
hi deeplizard, when i execute the code mentioned above in jupyter, it says 'FileNotFoundError: [Errno 2] No such file or directory: 'NN.PNG'.....can you please help.
thanks in advance
do we have to add NN.PNG somewhere, or is it generated by this code.
The code does not generate the image. The image was being read from disk to provide us with a visual.
code doesn't work anymore. model = sequential ([
Dense(5, input_shape=(3,), activation='relu'),
Dense(2, activation='softmax'),
])
Thanks, Minhaaj - We're working on getting this course updated 😄
In the mean time, the Keras course has been updated with the latest code (now fully integrated with TensorFlow) at the link below.
deeplizard.com/learn/playlist/PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL
I'd imagine the code you posted here isn't working perhaps due to not importing the Keras library from TensorFlow (new way now that it's integrated), as the underlying syntax for the Sequential model remains the same in the new Keras versions. (I also notice that your "s" in Sequential is not capitalized, but not sure if that was just a typo from writing into the comments.)
When you explained input_shape I got a little confused. Did you mean that input_shape=(3 ,) means that you have 3 input neurons or did you mean that each neuron in the hidden layer will receive 3 signals each? Or maybe that's the same thing?
Hey Christoffer - 3 input neurons. For example, if each sample in our training set was an array containing a person's age, height, and weight, then the network would have 3 input neurons. Each neuron represents a single feature from the sample. One for age, one for height, and one for weight.
Thank you! As a follow up question if you had for example 20 images of numbers (0-19) in this type of network would you only assign 1 neuron because all would be classified as numbers? I'm sure you would use another more effective technique for that but just so I understand correctly or maybe it doesn't work at all for that. I'm very new to deep learning so sorry if the question is confusing.
For images, each individual pixel is considered a feature. So, if you had a grayscale image that was 20x20 pixels in size, then in total you would have 20 x 20 = 400 total inputs to the network representing a single image. You might be interested in the videos/blogs below where we expand on this topic further when we cover convolutional neural networks (CNNs).
deeplizard.com/learn/video/k6ZF1TSniYk
deeplizard.com/learn/video/gmBfb6LNnZs
If you need a basic explanation of CNNs before jumping into those, then check this one out first:
deeplizard.com/learn/video/YRhxdVk_sIs
When running on Jupyter: "AttributeError: module 'scipy.ndimage' has no attribute 'imread'"
Im I missing some package or what?
Thanks!
CV2 or PIL
Thanks for another super informative video. I have one question: how are the weights of the connections between input- and hidden layers being determined?
Thank you for your time and shared knowledge :)
Hey Dragana - The weights are first randomly initialized, and then they are optimized during the training process with Stochastic Gradient Descent (SGD). SGD makes use of backpropagation to find the optimized weights. Don't worry if you're not yet familiar with these concepts. The videos below, which come later in this playlist, explain these topics in detail.
Weight initialization:
ua-cam.com/video/8krd5qKVw-Q/v-deo.html
Backpropagation (5 part series):
1. ua-cam.com/video/XE3krf3CQls/v-deo.html
2. ua-cam.com/video/2mSysRx-1c0/v-deo.html
3. ua-cam.com/video/G5b4jRBKNxw/v-deo.html
4. ua-cam.com/video/Zr5viAZGndE/v-deo.html
5. ua-cam.com/video/xClK__CqZnQ/v-deo.html
Show less
deeplizard Thank you!!! :)
Failed to run in google colab. problem: No such file or directory: 'nn.png'
nn.png is just an image of a neural network that I placed in the same directory as my jupyter notebook to print the image of the network. It is not required for anything else, but you can get an image of a network, save, and print it as well if you prefer.
@@deeplizard thanks for the quick response. Now I can figure out the issue.
As I'm using google colab, which is an online platform, so I'm facing the problem of directory. Can u kindly help me fix the issue? If your aren't busy, u can try the same code in google colab. Thanks.
what is NN.PNG???
It is just the name of the neural network image file that is being plotted in the notebook.
bigger texts wouldn't hurt.
I begin zooming in on the code in later videos :) Additionally, you can use the corresponding blog for each video at deeplizard.com. The text is easy to read there.
@@deeplizard Thanx for the quick reply. :D
Your videos are great, but far too fast.
Not sure if it's just me but I feel she is speaking on run-on sentences, and it gets hard to follow, and I get lost in the explanations
🎉🎉😢😢😢s
It worth noting that the information of this course is well structured and very clear. I can recommend this course to anyone new to machine learning. Thanks a lot for this course!
I found the answer to a question I had in a recent comment, so thank you for taking your time in replying even until today.
Hey @deeplizard,
Thank you so much for answering my question that even ChatGPT couldn't answer it clearly. I am actually a beginner in deep learning. Your explanation has answered my question. In fact, I have subscribed to your channel as well!
I love you.. You are the only source that explained ANN easily to me
I was struggling with the concept of dense layer a lot. This video makes it very easy. Thanks a lot.
Yeah me too. I think this material can be rather...dense.
😎Sorry I just had to.
dude these are incredible. thank you for the direct and conceise explanations.
Probably a dumb question, but in your neural network image, aren't we supposed to have an edge between the top node in the hidden layer and the bottom node in the output layer ?
Hey Yohann - Not a dumb question at all! You're right, there should be an edge there. I didn't notice that missing detail! In later videos of the playlist, we start using another network as our visual, so this missing edge won't persist for too long.
:0 I didn't notice
Thanks for the question, I had to browse ~50 comments to make sure that this missing connexion was actually a missing connexion
{
"question": "__________________ are used in models that are doing work with time series data.",
"choices": [
"Recurrent layers",
"Normalization layers",
"Recurent layers",
"Pooling layers"
],
"answer": "Recurrent layers",
"creator": "Kanishk",
"creationDate": "2022-11-13T06:06:16.798Z"
}
i just subbed bc of the channel name & pfp
🦎😎
Layers in a neural network? More like "Lizard, you make this material just work!" Thanks for teaching us in such a truly effective way.
Great
Hi,
I understand how layers work and everything, we give a number of inputs, do a few operations on layer to get a certain number of outputs in the end.
But how do you know how many hidden neurons to put? I looked online and couldn't find any explanation other than "it depends on what 'rule' you're using". So I can put how many I want? ok but what is the best number to use then? I found another saying "well, you try with more or less neurons and you check by hand". Is there any better way to find out ?
If you have an idea... :)
Ty
For those who follows the video use plt.imread right now, cause ndimage.imread is deprecated
Very informative, practical tutorials ever found. Thank you
@deeplizard..why did we used np.expand_dims and why we are plotting the img using index img[0]...i know it is a silly question to ask but i don't mind asking..
Amazing and crystal clear explanation. You are a great teacher. Do you plan to take a session on NLP
Thanks, Vidhya! NLP is on my list of potential topics for future videos!
awesome. Looking forward
It is sooo amazing tutorial. I love the way you explained. Thank you soo much
How to measure size of nodes in Hidden layer for example: Dense(units=5 .... ?
Thank you so much for these clear explained videos ❤️👍
Hi there, before asking my question I wanna thank you for your amazing and well explained videos, my question is: how do we specify the number of neurons in the hidden layer ? Do we choose whatever number of neurons we want or is the number of the latter in the hidden layer tied to something (which I don't know) ?
You're welcome, Lightning Blade! Generally speaking, the more complex your data, the more layers you can expect to have in your model, and the more neurons in each layer you'll likely need. Also, you'll typically see that the number of neurons increases within each layer as the layers become deeper in the network. There is not a general rule of thumb that I'm aware of to follow for choosing how many neurons to include in each layer. It's more of mixing trial and error along with experience from what's worked in previous models.
Thank you very much for the explanation, I'll make sure to watch all of your deep learning videos, I might have some other questions in the coming videos which I hope it won't bother you.
Thank you again.
This is great. Love this series. Hope to see more ML n deep learning tutorials
Thank you, ibrahim! Glad you're enjoying the series!
We have many other ML and DL series available here on UA-cam and on deeplizard.com.
You can browse all available series on the deeplizard.com home page!
This was useful video to understand layers, Thanks!
Thank you so much...Blessed to find this channel
Could you please create a playlist for Mask RCNN?
This is wonderful! Thank you so much.
Thank you !! This is exactly what I was looking for
AttributeError: module 'scipy.ndimage' has no attribute 'imread'
ndimage.imread() has been deprecated. Use matplotlib.pyplot.imread() instead.
@@deeplizard Thank you