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Cat and dog classification

This document discusses the application of convolutional neural networks (CNNs) for image classification, emphasizing their significance in deep learning and artificial intelligence. It outlines the process by which a computer recognizes images through pixel value arrays and gradual abstraction of features via convolutional layers. The dataset used for training includes 4000 images of cats and 4000 images of dogs, with an additional testing section of 1000 images each for validation.

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Neural Networks•
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Application to classifying the images in convolution neural network Prepared by: Omar AL-DABASH Cukurova University Computer Engineering Department
OUTLINE  Deep learning  Convolutional Neural Networks  The problem space  How can the computer recognize images  Our work
Deep Learning Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals to artificial Intelligence. The main aim of this learning is to help to achieve and understanding the data such as images, text and video to recognize them.
Convolutional Neural Networks Most of large companies uses this kind of deep learning at the core of their service. Facebook uses neural nets for their automatic tagging algorithms, Google for their photo search, Amazon for their product recommendations, and Instagram for their search infrastructure. However, use case of these networks is for image processing.
The problem space  When a computer sees an image (takes an image as input), it will see an array of pixel values. Depending on the resolution and size of the image. let's say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480 x 480 x 3. Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that point.  The computer is able perform image classification by looking for low level features such as edges and curves, and then building up to more abstract concepts through a series of convolutional layers.
Our work Dataset consist of three section 1- Training consist of: - 4000 of images cat. - 4000 of images dog 2- Test section consist of: - 1000 of images cat - 1000 of images dog 3- 4 images of single prediction Perhaps we put four images in single predication to testes the system learned or not.
Deep Learning Basics Deep Learning – is a set of machine learning algorithms based on multi-layer networks OUTPUTS HIDDEN NODES INPUTS
Deep Learning Basics CAT DOG Training
Deep Learning Basics CAT DOG
Deep Learning CAT DOG
Cat and dog classification