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PROJECT


Cats vs Dogs Classification with Convolutional Neural Networks

Build a classifier for distinguishing between cats and dogs in images using a convolutional neural network implemented with TensorFlow.

Cats vs Dogs Classification with Convolutional Neural Networks

You will learn to:

Train and test a convolutional neural network.

Load an already trained neural network.

Preprocess a dataset containing images.

Visualize the performance of a trained model.

Skills

Deep Learning

Machine Learning

Prerequisites

Intermediate understanding of Python

Basic understanding of image processing

Intermediate knowledge of machine learning

Basic understanding of deep learning models

Technologies

NumPy

Python

seaborn

Tensorflow

Matplotlib

Project Description

This project aims to build a binary classifier to distinguish between the images of cats and dogs. For this purpose, we will build a convolutional neural network (CNN) using TensorFlow, a popular open-source software library for machine learning. CNNs are a type of deep learning algorithms that are particularly effective for image classification tasks. They consist several layers that perform convolutions and pooling operations on the input images to extract important features.

We will use a dataset of images containing cats and dogs. This dataset is split into training, validation, and testing sets for training and evaluation purposes.

Project Tasks

1

Introduction

Task 0: Getting Started

2

Load and Preprocess the Dataset

Task 1: Import the Libraries

Task 2: Load the Dataset

Task 3: Visualize the Dataset

3

Build the Convolutional Neural Network

Task 4: Create an Instance of the Neural Network

Task 5: Adding the Layers to the CNN

4

Train the Convolutional Neural Network

Task 6: Compile the Model

Task 7: Train the CNN

5

Test the Convolutional Neural Network

Task 8: Test the CNN

Task 9: Visualize the Metrics

Task 10: Plot the Confusion Matrix

Congratulations!