Image Classification Using Deep Learning and Tensorflow
- Guided Project
- 2 hours
- Intermediate
- Python
Practice building an image classifier using Python, TensorFlow, and Keras to develop computer vision skills.
Overview
In this project, you'll assume the role of a computer vision engineer and build an end-to-end image classification system to identify dog breeds. Using Python, TensorFlow, and Keras, you'll train a convolutional neural network on a dataset of dog images.
Throughout the project, you'll learn to load and preprocess image data, build and train deep learning models, and optimize model performance to reduce overfitting. By completing this project, you'll gain practical experience in computer vision and develop a strong portfolio piece demonstrating your ability to apply deep learning to real-world problems.
Objective: Develop an image classification system using deep learning to accurately identify dog breeds, showcasing your skills in computer vision and neural networks.
What You'll Learn
- ✓ Build an end-to-end computer vision project using JupyterLab, TensorFlow, and Keras
- ✓ Train a convolutional neural network to classify images
- ✓ Improve the model performance
Before You Start
- ✓ Working with variables, data types, and control flow in Python
- ✓ Manipulating and analyzing data using the NumPy and pandas libraries
- ✓ Exploring and visualizing datasets using Python libraries like matplotlib
- ✓ Understanding fundamental machine learning concepts and terminology
Project Steps
9 steps
- 1 Project Overview
- 2 Setting up the Project and Loading the Data
- 3 Exploring Images in the Data
- 4 Training an Initial Convolutional Layer
- 5 Looking at the Model Error
- 6 Improving the Model Accuracy
- 7 Augmenting the Data
- 8 Investigating the Model Error
- 9 Conclusions and Next Steps
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