Dog vs Cat Classification Using Convolution Neural Network | Python Data Science Day

In this presentation, we delve into a Convolutional Neural Network (CNN) project designed for the classification of images into two categories: dogs and cats. CNNs are a type of deep neural network particularly adept at image recognition tasks. Our goal is to showcase the intricacies of the CNN architecture and its application in building an effective and accurate classifier for distinguishing between these common pet species.

The CNN model is a multi-layered neural network that employs convolutional layers to automatically learn hierarchical features from input images. These convolutional layers are supplemented by pooling layers, which downsample the spatial dimensions of the learned features, and fully connected layers for making predictions. Our dataset consists of labeled images of dogs and cats, serving as the training material for the CNN to learn and generalize patterns.

Ultimately, this CNN project exemplifies the power of deep learning in image classification tasks and serves as a foundation for understanding the broader applications of neural networks in computer vision. Even if you do not have any prior experience with deep learning, I urge you to come join me, and witness the many wonders of Deep Learning and CNN in particular.

Chapters:
00:00 Dog vs Cat Classification Using Convolution Neural Network
01:29 Today's Agenda
02:40 What is Convolution Neural Network (CNN)
03:50 Why CNN? Why Not ANN?
07:52 Typical CNN Architecture
11:10 CNN Applications
11:57 Demo

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Featuring: Jyothi Swaroop Makena, Deep Learning Enthusiast and Full-Stack developer (  / jyothiswaroopmakena  )