continue making videos

while running the code I am getting all loss values as NaN, filepath I have mentioned as suggested in runtime checkpoint_path = '/tmp/ckpt/checkpoint.model.keras.weights.h5'

Thank you for this explanatory tutorial. I have also followed the same code for building ANN model on regression data having 4 input variables and 1 output variable and the model is built successfully. But now, I want to optimize the ANN model's solutions by Genetic Algorithm (GA). So, for this I am following "kzbin.info/www/bejne/opvNl6WofdGredksi=-DBs2Bki0p1LgABm", this tutorial as a reference but, they have used Random Classifier in place of ANN model to optimize their regression data. So, at their 19th step, they are calling their trained model into a new variable which is used later as a "fitness function" for GA. I am using "model_full = ann_viz(model, view=False, filename= 'network.gv', title='My Neural Network')" instead of their Rnadomforest classifier. So, please help me in this as I am getting error in "input_shape" i.e., "AttributeError: 'Dense' object has no attribute 'input_shape'". Although I have changed the input_shape as mentioned by you while building ANN modelto "4" as I have 4 variables.

thank you such amazing tutorial

hello i have a problem when i run the code with same data that you give, i got NaN value for loss mae mse mape val_loss val_mae val_mse val_mape

Thanks

This is one of the best lectures on this topic. Great work.

My jupyter code on line 2 pd.read_csv('iris_dataset.csv') error "FileNotFoundError". what yould i do?

very helpful sir

What if configuration are given : input data features 20, Hidden layer 1 - 10 neurons. How can we load this data ? Code or syntax

Thank you for this. Can you show us how to get the result of a specific input?

wow

Nice video.

Excellent work! Thanks!

Why did you define your own function for the normalization? Is not it equivalent to the StandardScaler() method from SKLearn?

can model.fit() be used on multiple datasets. For example, model.fit(normed_training_data1, train_labels1) model.fit(nromed_training_data2, train_labels 2) y_pred = model.predict(normed_test_data) I have 2 datasets that I want to train for the model

Thank you for the video; most places, including sklearn, show (X_train, X_test, y_train, y_test = train_test_split), why does your model have only two of them? is it because you separate the validation, or does it have any other reason?

Great video! Can you please provide the code.

For lines 20 & 21, what is the benefit of using validation?

Wow... The best hands-on explanation in youtube so far. Good work bro. Thanks a lot. Learnt a lot!!

please give the github repo

Hello, your video is very good! But may I ask a question? Is this multi-class classifier is using OVO(One vs. One) or OVA(One vs. All) or RCC?

so no one-hot encoding is needed?

Nice video. I followed your tutorial and it worked brilliantly on my project. On thing at the end, did you made a mistake on the confusion matrix? it seems like you are plotting predicted results against predicted results.

thank you, im wondering if we can get the code for this in github or something ?

Great Video! Please share tutorial on Github and share the link in description. Thanks

can you upload videos for Alex Net for multi class in python

can this be apply for multi-label classification too? if yes, where is the part i can change the algorithm to multi-label ?

Thanks for very good video , may I know, where to get the script and also dataset? Do you provide that or available somewhere. Thanks sir

Good video! Help me a lot. Pleaseeee keep doing them

Great video. Thank you.

can i have the code

Very nice!!!

Amazing! Please continue on giving tutorials for us!