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PROJECT
Deep Learning with Keras for Landscape Recognition
In this project, we’ll learn how to train an image classification model using Keras. We will select the correct model, create the data loaders, configure the hyperparameters, and train, evaluate, and test the model on our image data.
You will learn to:
Visualize and examine the image data.
Create the data loaders for training and validation.
Select the correct DL model for the task.
Define the training configuration (loss function, optimizer, etc.).
Train the model and evaluate the performance.
Use the trained model to classify new images.
Skills
Deep Learning
Computer Vision
Data Visualization
Convolutional Neural Network
Prerequisites
Basic programming skills in Python
Intermediate working knowledge of Keras
Intermediate knowledge of deep learning theory
Basic knowledge of Matplotlib and NumPy
Technologies
![keras logo](/static/imgs/TechIcons/keras.png)
Keras
Numpy
Python
Matplotlib
Project Description
Deep learning is the most prominent AI technology of the last few years, with outstanding results in computer vision and natural language processing. Keras is among the most popular and user-friendly frameworks for training deep learning models used in academia, industry, and by enthusiasts worldwide.
In this project, we will use Keras on a computer vision task of image classification. We will use landscape images from three different categories (city, mountain, beach) and perform all the steps of the training process. First, we will investigate/understand our data. Next, we will create data loaders for training, validating, and testing the model. Then, we will select an appropriate vision model, a loss function, and an optimizer. Moreover, we will train the model on our data and monitor the performance. Finally, we will use our fine-tuned model to classify new images.
Apart from Keras, we will additionally use NumPy for data manipulation and Matplotlib for visualization.
Project Tasks
1
Introduction
Task 0: Get Started
Task 1: Import the Packages
2
Data Analysis
Task 2: Explore the Structure of the Directories
Task 3: Visualize the Image Data
3
Data Loaders
Task 4: Create a Data Loader
Task 5: Inspect the Dataset Output
Task 6: Create the Training and Validation Datasets
4
Create and Compile the Model
Task 7: Create the Model
Task 8: Define the Loss Function and the Metrics
Task 9: Compile the Model
Task 10: Train the Model
Task 11: Evaluate the Model
Task 12: Use the Model on an Unseen Image
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.