Using Neural Networks

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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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Look inside
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.

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

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