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Hey Sr, thank you for the video. If you allow me to ask you some questions, why do we have, while train the model, pass the X and the Y? Is the model reconstructing the original sequence and trying to predict the next value based on the 30 values provided? (I am asking because I was expecting that we would bass the same sequence, something similar as we perform using a vanilla autoencoder). It seems that we input a sequence, tries to predict the next for the given sequence as we reconstruct the initial sequence. When we calculate the error, the error is based on the reconstruction process am I right? Thank you in advance!

thanks a lot sreeni really useful!

Please sir do model building from scratch by doing segmentation using deep learning model and then classification of objects in an image also by deep learning. Waiting for ur video .Jazakallah for ur time n efforts. May ALLAH BLESS U ALWAYS AMIN

Asslamoalaikum sir I am ayesha from Pakistan. I have learnt alot from ur tutorials. Thank u so much for ur efforts and time. Please provide some videos on segmentation of images using deep learning model like signet, unet etc.

Sir, Please make a video on the cityscapes dataset for segmentation purpose.

How do I learn what number to set the artificial neurons in each layer? i'm super confused.

Sir can you please demonstrate hybrid optimization algorithms PSO and Firefly to demonstrate exploration and exploitation

Thank you very much, sir. This video is realy good introduction. I will keep studying and learning image processing with python through your lectures. Again, thank you so much for learning this valueable things in an easiest way.

Can I use this techniques of segmentation for cracks and damage segmentation on the walls or concrete

Awesome explanation

thank you so muchhhhhh

Great work! I was searching for the exact thing for my research and this is helping me a lot and saving my time. Your explanation is very clear and informative. Thank you for your time.

Hi i have used your code in order to fine tune sam in order to segment aerial images , but when i use my finetunedsam.pth it doesn’t even segment the images that it used to segment with no finetuning, what do you think is the problem ? Thank you in advance !!

Dear Dr. Sreenivas, your video has always been helpful. I wonder if I am trying to blend my coordinated rasters that only contains probability from 0 to 1 instead of RGB, is it possible to use rasterio instead of opencv? Thank you.

🎯 Key points for quick navigation: 01:40 *🛠️ Understanding the U-Net architecture is crucial before building it from scratch in Python.* 02:21 *🌐 Image dimensions in U-Net can be adjusted to simplify understanding, such as using 256x256 input sizes.* 03:58 *🖼️ The U-Net model consists of repeated patterns of convolutions, max pooling, and upsampling layers.* 06:47 *🔗 Concatenation of features in U-Net aids in semantic segmentation by combining context from different scales.* 08:51 *🔄 Skip connections in U-Net allow features to be combined across network levels for improved semantic segmentation.* 12:49 *🧱 Defining functions for convolution and encoder blocks in U-Net helps streamline the model construction process.* 16:30 *🔄 Decoder blocks in U-Net consist of upconvolution, concatenation, and convolution operations.* 19:29 *🧩 Building the U-Net model involves defining encoder blocks, base block, and decoder blocks systematically.* 22:58 *🛠️ Focus on the decoder block with inputs from the previous block.* 23:37 *🖥️ Output layer in U-Net is a convolution with one output using sigmoid activation.* 24:32 *🧱 U-Net architecture divided into convolution, encoder, and decoder blocks for image segmentation.* 28:02 *🚀 Model building process includes defining encoder, decoder blocks, and building the U-Net architecture.* 33:12 *🔄 Use data augmentation to improve results in image segmentation tasks.* 34:24 *📊 After training, check validation metrics like accuracy and IOU score for model performance evaluation.* Made with HARPA AI

Is there a chance to get processed files, that have gone through the all initial steps up until model itself?

Nice video. Thanks for sharing!!!

nice content, thank you!

Your videos are too good. One of the best teaching . Calm and detailed

Thank you so much this is really helpful!! Bless you ^^

@DigitalSreeni I have been following your videos a lot and wanted to ask if you could do a model building from scratch? (creating the algorithm) Also can't wait for a mechanical engineering series!!

that was crystal clear, thank you for the good explanation!

very useful information, ty sir

Thank you very much for the informative videos. I would like to ask if we can convert yolov4-darknet bounding boxes to the Mask RCNN format

35:32 In this part I receive an error: AttributeError: module 'keras.api.backend' has no attribute 'sum' ... can some1 help me with this?

very interesting. clear explanation too

what if the number of trains Found 1896 images belonging to 12 classes. test Found 528 images belonging to 12 classes. and validate Found 276 images belonging to 12 classes. what is the epoch, batch size and patience so that the training & val accuracy and Training and val loss plot graphs match and are close together?

bro trying to share about life n forgot wts hes teaching 🤣🤣🤣🤣 only were i gt complete idea about xgboost tq

man, I didnt even watch the video I just checked your channel and here I am, hitting the subscribe button..

hello sir. i created app password but my google crome not showing options as 4.10 time as in your video. i have two step verification also.

Please make a video on this topic - patch based inference

Please make a video on this topic - patch-based inference

Sir you're really good. I always learn something new from your valuable tutorials. Sir, Can you make two playlist for GNN image segmentation and another one is for Quantum machine learning ? . Maybe those are help us to know better update topics. Or more topics are respectively active learning, one short learning, equivariant neural network etc.... Thank you so much for your time and response sir.

thank you for this tutorial, great and clear explanation!

Very informative video as always thank you! I have a question, I saw the image you fed into Qupath was in jpeg format. But does not using such format instead of tiff would lead to loss of data for training an ML model for scientific purposes? Especially when using high bit-depth microscopy images. I am curious what would be the difference in outcome of detection when training using tiff and jpeg each.

Sir you are the best. I cant thank you enough. Before your video i could not understand this concept at all. You saved me. Wish you all the best.

After each epochs why the numbers are changing🤔?? I didn't got it..Isnt it a mode Collapse part??.. I tried with face image dataset and it did actually that it mixed up and same outputs were given even if different outputs were provided to it.

Thanks for your amazing presentation!

very clear and informative video as all the time. can you make a video on PSO with image dataset used for hyper parameter training and neural network optimization.

I keep getting an error where it says NotImplementedError: Exception encountered when calling Lambda.call(). We could not automatically infer the shape of the Lambda's output. Please specify the `output_shape` argument for this Lambda layer. Do you know why I am getting this problem?

Can you make a constraint Genitic Algorithm

absolutely hilarious course , thanks a lot for covering this topics

Could you share your default cell image?

super helpful, many thanks 🙏 just wanted to add: for converting to coco annotations to mask annToMask() function from pycocotools comes in handy as well. :)

Sir extremely grateful for your efforts

@DigitalSreeni Sorry, I did not understand the purpose of using genetic algorithm in this video. Can you please explain? Thanks.

can anyone give me this code ? I can't find it in Github

I am having this issue: 'Sequential' object has no attribute 'fit_generator'