Two-Project Series
Art Style Transfer with Deep Learning you own this product
- prerequisites
- basics of TensorFlow • basics of Keras • basics of scikit-learn
- skills learned
- mathematical operations on images • implementing loss functions • transfer learning
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
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.
choose your plan
pro
monthly
annual
$24.99
$249.99
only $20.83 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
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Art Style Transfer with Deep Learning project for free
Art Style Transfer with Deep Learning project for free
team
monthly
annual
$49.99
$399.99
only $33.33 per month
- five seats for your team
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose another free product every time you renew
- choose twelve free products per year
- exclusive 50% discount on all purchases
-
![]()
Art Style Transfer with Deep Learning project for free
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 the following:
TOOLS
- 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
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.
