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Art Style Transfer

Using Neural Networks you own this product

This project is part of the liveProject series Art Style Transfer with Deep Learning
prerequisites
basics of TensorFlow • basics of Keras • basics of scikit-learn
skills learned
convolutional neural networks with TensorFlow and Keras • analyzing model performance with scikit-learn • visualizing filter and class maximizations with keras-vis
Rajeev Ratan
1 week · 8-12 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Art Style Transfer with Deep Learning liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products.

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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.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

project author

Rajeev Ratan
Rajeev Ratan is a data scientist, computer vision consultant, and researcher. He has spent the last five years working at several computer vision startups, and has created several popular online courses on OpenCV and deep learning convolutional neural networks. He holds an MSc in Artificial Intelligence from the University of Edinburgh and has published research on using data-driven methods for Probabilistic Stochastic Modeling for Public Transport.

prerequisites

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:

TOOLS
  • Basics of TensorFlow
  • Basics of Keras
  • Basics of scikit-learn
  • Basics of Jupyter Notebook
TECHNIQUES
  • 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

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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