Convolutional Neural Networks (CNN) Implementation with Keras - Python

Hey guys what is the most easy way to run CNN if no GPU?

Please is there like a video on just using cnn with kaggle dataset in predicting Heart Disease ?

Excellent, well explain video. Thank you!

Not good, your audio and video doesn’t sync well. You are typing something else at point and speaking about something else.

where can i download mnist.csv file as in a video? Anyone help me pleaseee

can you say how to load a image dataset from our pc

You are Confused My friend a lot😂

merci

sir how we can get the csv dataset file?

I think you could code during the explain, you explain about the idea of the things but don't talk about the code itself.

Where can I fing the mnist.csv file ?

hi sir i want to extract the features after training data
i have done all these steps and now i want to extract features for real life application and i want to find the features of my data from cnn

Great Tutorials.
PS: your voice is not synchronized with video kindly look into it.

how to put in code: 1-D Conv 2-DConv 3-DConv 4-DConv?

I need help with 'input_shape' variable. My data is 1d. Not 3d like yours. How to pass it?

it is better if u have used predict function and also describeed but still best explaination man

great tutorial love from iit kanpur

bro you are the best!

Where can I obtain the mnist.csv file???

The belows kind of error has shown...
NameError: name 'data' is not defined. whats the solution?

thanks alot. Can you refer to find out the dataset mnist.cvs that you are using? Where I can find such a data. Thanks

Thank you sir, just one question where did u define the number of epochs?

Thank you very much. could you also help us by posting your github link for this code

Great tutorial. Can you please make the next part of this video where we can draw a number and this learned CNN will predict that?

Added English captions !
Happy Learning :)

Great works. Can you please do video about functional api for ConvLstm please ?

thank you very much,
I will wait you for RNN, LSTM :)