Convolutional Neural Network Explained | CNN | Deep Learning

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Learn basics of Convolutional Neural network and what are the types of Layers in CNN.
Also Learn What is a Convolutional Neural Network and how does it work? Learn about the history of CNNs and the most popular Convolutional Networks Architectures.

Convolutional neural networks are very important in Deep learning.
Convolution neural network (also known as ConvNet or CNN) is a type of feed-forward neural network used in tasks like image analysis, natural language processing, and other complex image classification problems. If you want to do computer vision or image recognition tasks, you simply can’t go without them.

How does a CNN work?

A convolutional neural network, or ConvNet, is just a neural network that uses convolution.
Convolutional neural networks apply a filter to an input to create a feature map that summarizes the presence of detected features in the input.

The most common CNNs are:

1. LeNet-5 (1998)

2. AlexNet (2012)

3. ZFNet (2013)

4. GoogleNet / Inception(2014)

5. VGGNet (2014)

6. ResNet (2015)

If you have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer your queries.

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Best ever lecture on CNN layers. Thank you so much mam for such a fantastic lecture. it helped me a lot. many of my doubts get cleared today.

shubhamsongire

Great explanation! Its really very clear! Thank you!

sathwikmittapalli

Best tutorial on Convolutional neural network

ashimasingla

very helpful content with easy explanation huge respect miss.

MuhammadHaroon-lzxf

superb explanation and never understood prior CNN like this. hatsoff to you mam

prasadinipadwal

Best lecture on CNN, Thank you so much maam. Your lecture help me alot. Just waiting YOLOv8 algorithm explanation.

c.lalnunkima

simple expiation and easy to understand the mechanism. Could you please make a detail video on Domain Adaptation?

oneer

Amazing Explanation on CNN. This tutorial helps me a lot in my semester exam preparation for deep learning. Thank you 🙂

dhanashrisaner

Thank you mam for explaining it in such a simple way. It helped me a lot in understanding it. I hope you will continue this nice cause for the benefit of the masses.

muhammadsabir

underrated channel, better than @krishnaik channel

kunalfrombihar

Kindly mention how output image chennal number is decided when input image has more than one chennal. ?? Or how Convolution layer will work for 3d image s or colour images

RAHUDAS

That is the best explanation I saw for CNN thank you so much, you really explained it very well (y)

asmadjaidri

thank you very much, your explanation is really great! simple and easy to understand

rajabmur

Best lectures recommended to new students

ARCGISPROMASTERCLASS

Thank you so much mam for such a fantastic lecture. it helped me a lot. many of my doubts get cleared today.

renukanellaturu

you explained better than other youtubers.... <3

shivalayayadav

Hi.. great LeLecture. can you share these slides?

shahinzamanzoor

madam how u calculated 1024 (FC) can you info?

dr.aravindacvnmamit

excellent explanation madam.thank you so much

ravulajyothsna

could you plz show the calc of the fully connected layer?

shilpsshilpa

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