Відео

RNNs, LSTMs, and Bi-LSTMs are widely used for tasks such as sequence classification and language translation. However, a major limitation of these models is the significant computational time required because they process input sequences one element at a time. To address this issue, Google Brain developed the Transformer model, which uses a multi-head attention mechanism. This allows the model to focus on multiple parts of the sequence in parallel, drastically reducing the computational time and improving efficiency.

Hi if it possible, Can you please provide the link for google colab?

happy new year!

thanku madam, keep doing this kind of good videos

Could you please share the code file in the description here?

I like the way you teach

I have a question about using pretrained models like you. Pretrained models are based on ImageNet. However, ImageNet doesn't include medical images of brain tumors. Therefore, I think we shouldn't use weights='imagenet', and layer.trainable should be set to True. Could you please provide me with your feedback?

This is the worst lecture I have ever received. like literally you have repeated every single line 10 times. why so much redundancy? we are adults, we can understand a concept or listen to words when you're telling for the first time. Please speak Hindi if you are not good at it and fluent in speaking correct English sentences. so much frustrating.

Hello, ValueError: The name "functional_1" is used 2 times in the model. All operation names should be unique. can you help me with this error

you explained very well ❤

ma'am since the dataset contains only 2 classes i.e either tumor or nor then why are you using Categorical cross_entropy with 2 neurons why cant we use binary cross_entropy with 1 neuron?

Thank you! This has really helped me understand AVL Trees better. However, in the Final Tree, you forgot to add 8.

Mam where it is used to classify whether the person have tumor or not Like how can i use this model on new images later. Please tell me as this will help me building new stuff for mini project in college

Copy API command is not visible..

Is this deep learning

Thank you so much

Probably the most important video for beginners trying to create binary classifiers. Thanks amilli 👏

can i show new imagen to this model so the model can tell me if a person have a tumor or not? since the dataset just have two seption ppl that have a tumor and people that doesnt have a tumor, it will be cool if you add imagen withour any context to see if the model can guest if a person have a tumor or no.

your videos are very helpful.can you please provide the code mam.

can please provode full code..

TypeError: Layer.__init__() takes 1 positional argument but 4 were given

I have written this code; however, I am encountering an error with this particular line. Could you kindly assist me with this? "ensemble_output= WeightedAverageLayer(0.6,0.3,0.1)(models_outputs)" the error "TypeError: Layer.__init__() takes 1 positional argument but 4 were given"

Good explanation

Please show the visible images about check point

Thank you mam very useful

Hi Maa'm, How features are extacted and AOI calculated? Does Inception V3 do this task automatically... if so how can you please explain?

Thank you so much for this. It's explained really well with the example

Can I contact you via email to inquire about some things?

Might be model is overffited!

what accuracy does this model get

straight forward, clear explanation.

Excellent

very less time to cover more helpfull info , thanks u

What a great instructor you are! Thanks

Very nice

Very nice

Thank you mam for the great video! Some doubts about feature scaling. 1) Shouldn't we do the feature scaling after train_test split? 2) the code you suggested will do the feature scaling of all the label encoded variables also. Shouldn't we do the feature scaling of only numeric variables?

Thanks for the wonderful video mam! I have a doubt. Do we require to do feature scaling of all numerical variables before fitting the models or these models take care of it automatically?

thank you so much mam.. super clear cut explanation mam..kindly upload image to text genration also from generative ai mam kindly

6:55, is this on testing data?

Hi mam enakku DL la orusila doubts irukku mam Unga insta id kudunga mam Naa voice note mooliyama explain panren. Enakku help pannunga mam please mam please

In weighted ensemble model shall we give weights as sum of 1 or any random in nature, one thing is a clear based high accuracy provided by the model you can give high weightage value . am i correct madam , please give me a reply as soon as possible.

❤

Thanks

Hello. Hope you are doing fine. I am an undergrad student . I am working on a project. Can you guide me in a query. I have CT scan dataset in the form of DICOM meaning for a single CT scan I have 100 layers. Should I feed the whole data into my deep learning model or should I feed the single malicious layer into my model. Thankyou

Good one. Please make a Deep learning series as well!

straight forward , simple and clear! Thank you!

THANKS YOU FOR THIS

Congrats! I'm learning a lottt with you. You are so a good teacher! Thanks a lot!

thank you for this