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Thanks for the video for someone like me struggle in beginning with NLP !!!

Sir why u have stopped to make videos ? U were too good if u can please continue sir.

00:00 📘 Introduction: Professor Stugard explains the goal is to understand convolutional neural networks, focusing on image recognition. 00:13 🖼 Image Classification: The process involves inputting an image, using the CNN's hidden layers, and outputting a class guess. 00:56 🔄 Convolution: Uses filters to create feature maps and activates them with the ReLU function for image classification tasks. 01:37 🌊 Pooling: Generalizes feature maps to detect features in multiple areas, creating pooled feature maps. 02:19 🔁 Iterative Process: Multiple convolution and pooling cycles lead to a fully connected neural network for outputting image class guesses. 03:02 👁 Biological Inspiration: CNNs mimic human vision by finding features in images rather than analyzing every pixel. 04:11 🧠 Feature Extraction: CNN layers break down images into areas for convolution and pooling, simplifying feature representation. 05:09 📊 Data Efficiency: Convolution reduces data size, making it easier and faster to process, crucial for high-resolution images. 06:42 ➗ Convolution Mechanics: A kernel applied to an image matrix reduces its dimensions, efficiently preserving feature information. 08:21 🔍 Feature Detectors: Different kernels and feature detectors extract various features like edges or enhancements. 10:13 ⚙ ReLU Activation: Facilitates non-linear classification by mapping negative values to zero, enhancing training. 12:04 🌀 Max Pooling: Reduces data size by selecting maximum values in non-overlapping regions, aiding in feature generalization. 13:27 ✔ Importance of Generalization: Pooling allows CNNs to recognize features despite transformations like rotation or scaling. 14:22 📉 Size Reduction: Max pooling can significantly decrease data size, even from 100 to 9 values, without losing general feature recognition. 16:51 ➖ Flattening: Pooled feature maps are flattened into vectors before being input into standard neural networks for learning. 18:52 🚗 Applications: CNNs are used in diverse applications like self-driving cars, facial recognition, and botanical identification. 19:47 🔄 Training with Epochs: Iterative process involving multiple epochs enhances model accuracy through repeated weight adjustments. 21:25 🏆 Accuracy: High accuracy is vital for critical tasks; CNNs require extensive training to achieve such precision

Hello! thank you for the video, it helped me a lot to understand how Naive Bayes is works!

thank you Mr Stugard, the video is really helpful for my assignment on ethical issues

Thanks. You really helped me)

yours videos are awesome. please do SEO of your channel and video. That would help to attract audience

Looking forward for logistic regression video

I still couldn't understand why we can use volume enclosed by the surface to represent surface integral for scalar field

Great stuff, Professor!

Great video! While playing around with the messages I found the model decided "we have been trying to reach you about your cars extended warranty" is ham haha. Other messages were no issue though.

where is the code file?

This was such a well explained video, thank you for this you are an amazing teacher!

Basics well explajned

Great technical delivery from your side, you easily made difficult topics easy and also connected dots wonderfully. Thank you a lot

I really hope someone will HELP me in my case so I built a similar spam detector for my college project but my professor is saying that it is a data science project that is why I want to give it a touch of cybersecurity so what should I add in this project to make it more specific to cyber security ?

Awesome Video Learned a lot

that was just awesome, love you from Azerbaijan Baku <3

Legend

Such a simple and greatly explained video. Thanks man

Can u please provide the code 🙂🙂🙂🙂

Hoe to deploy this plsss

Can you provide github project link containing full source code

Very interesting! Good recommendations.

Will you provide github project link For full souce code

hey there nice explanation Thanks a lot ! nicely explained and easy to understand wish we had professors like you in our college <3

very awesome video and demonstration! Insane to me how this works. one of those things as CS student that gets me excited!

jesus christ, talking about niche videos, tysm for this video!!!

Excellent

This is naive video, i understood whole concept in just 30 minutes. Thank you.

is this realated with cloud coumputing or general mails??/

Thank you man!

Awesome !!

It's not a bad project like this. To see the data loading and preparations step lined out is very nice. But I came here to learn about Naive Bayes and how those calculations work, and all I got was MultinomialNB().

Hi, thanks a lot for the video. It is very informative and very well explained. I have a curiosity, where did you get the email database from? Thank you in advance.

thank you, i can learn a lot from you

So I have gone through your entire videos And trust me as an engineering student you have awesome videos. But if you can focus your teaching with project based then you will have a lot of views Example the videos your have on linear regression, support vector machine and the rest But this is amazing Thanks so much

Hi there. Good video. Please, what screen record did you use ?

Thank you for making this video 😊

Great video mate, you stand out

please can you provide the link of written script

Thank you so much sir ☺️

why when i upload the dataset make this eroor Error tokenizing data. C error: Expected 2 fields in line 13, saw 4

Please can you link the dataset you used. Really good video btw. Very well explained.

Nicely explained... thanks

thanks, very good. What if there is new data outside the dataset, can it be detected? How to?

This was exactly what i needed

Highly underrated video. This channel is an undiscovered GEM!

🤔 What

waiting for more...😄