In this liveProject, you’ll follow research laid out in a groundbreaking paper and work with algorithms that can take the aesthetic style of one image and apply it to another. You’ll use convolutional neural networks and transfer learning to build a simple image classifier and implement a neural style transfer. You’ll use TensorFlow and Keras to build your networks, Matplotlib and keras-vis to visualize them, and scikit-learn to analyze your results.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
This liveProject is for intermediate Python programmers looking to enhance their data science skills with image manipulation techniques. To begin this liveProject, you will need to be familiar with:
- Basics of TensorFlow
- Basics of Keras
- Basics of scikit-learn
- Basics of Jupyter Notebook
- Basics of computer vision
- Basics of deep learning
- Basis of linear algebra
you will learn
In this liveProject, you'll utilize popular Python deep learning tools to build artistically-inclined algorithms. These popular tools and techniques are easily applied to other deep learning tasks common in industry.
- Building convolutional neural networks with TensorFlow and Keras
- Analyzing your model’s performance with scikit-learn
- Visualizing filter and class maximizations with keras-vis