Two-Project Series

Art Style Transfer with Deep Learning you own this product

basics of TensorFlow • basics of Keras • basics of scikit-learn
skills learned
mathematical operations on images • implementing loss functions • transfer learning
Rajeev Ratan
2 weeks · 8-12 hours per week average · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

In this series of liveProjects, you’ll explore the capabilities of an AI algorithm to create beautiful art. You’ll create fun tools that can make photos look like paintings, and also augment image datasets for training other AI. Through hands-on machine learning projects, you’ll explore the latent space of a deep neural network, and manipulate its values to see how it affects an input image. You’ll tackle challenges such as training an image classifier, manipulating your filters to produce dreamlike images, and creating AI-generated images that look like human art.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

liveProject mentor Stefano Masneri shares what he likes about the Manning liveProject platform.

here's what's included

Project 1 Using Neural Networks
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.
Project 2 Generate Art
In this liveProject, you’ll replicate Google’s Deep Dream algorithm to explore the artistic creations of a neural network. You’ll start by investigating the latent space of a deep neural network and how manipulating its values can affect an input image. You’ll visualize inputs that maximize your filters, and manipulate these filters to produce ‘dream-like’ hallucinations.

book and video resources

When you start each of the projects in this series, you'll get full access to the following book and video for 90 days.

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  • Art Style Transfer with Deep Learning project for free

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.


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 the following:

  • 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


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.