Art Style Transfer Using Neural Networks

prerequisites
Intermediate Python, Beginner TensorFlow and Keras, Basics of Computer Vision, Basics of Deep Learning
skills learned
Build a CNN, Image manipulation techniques, Transfer Learning
Rajeev Ratan
6 weeks · 7-10 hours per week · INTERMEDIATE
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In this liveProject, you’ll explore the capabilities of an AI algorithm to create beautiful art. Following research laid out in a groundbreaking paper, you plan to create an algorithm that can take the aesthetic style of one image and apply it another. This fun tool can make photos look like paintings, and also augment image datasets for training other AI. To create this AI, you explore the latent space of a deep neural network, and manipulate its values to see how it affects an input image. Your challenges will include training an image classifier, manipulating your filters to produce dreamlike images, and creating AI-generated images that look like human art.
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, and 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
  • Mathematical operations on images
  • Implementing loss functions
  • Transfer learning
  • Model manipulation

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.

project outline

Introduction

Prerequisites Test

Introduction Video

Start Project

1. Training a Simple Image Classifier using Convolutional Neural Networks

1.1 Training a Simple Image Classifier using Convolutional Neural Networks

But What is a Neural Network?

Fundamentals of Machine Learning

1.2 Submit Your Work

2. Understanding what Convolutional Neural Networks Learn

2.1 Understanding what Convolutional Neural Networks Learn

Visualizing What Convnets Learn

2.2 Submit Your Work

3. Transfer Learning & Feature Map Visualization

3.1 Transfer Learning

3.2 Feature Map and Filter Visualization of a Pretrained Model

Using a Pretrained Convnet

Transfer Learning

3.3 Submit Your Work

4. Visualizing Filter Maximizations, Grad-CAM, and Class Maximization

4.1 Visualizing Filter Maximizations

4.2 Grad-CAM Visualizations

4.3 Class Maximization

How Convolutional Neural Networks See the World

4.4 Submit Your Work

5. Implementing Google’s Deep Dream

5.1 Implementing Google’s Deep Dream

Deep Dream

5.2 Submit Your Work

6. Implementing Neural Style Transfer

6.1 Neural Style Transfer Loss Functions

6.2 Neural Style Transfer Algorithm

Neural Style Transfer

6.3 Submit Your Work

Summary

Project Conlcusions

FAQs

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