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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!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.