Train a DL Model

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prerequisites
intermediate Python • basics of pandas • basics of scikit-learn and Keras
skills learned
use Keras to interface with TensorFlow • utilize Keras callbacks • track and assess model performance
Mark Ryan
1 week · 10 hours per week · INTERMEDIATE
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catalog / Data Science
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Look inside
In this liveProject, you’ll create and train your deep learning model to predict where a new Airbnb listing will be priced above or below average pricing. You’ll put pandas, Keras, and scikit-learn into action, and master key deep learning techniques for tracking model performance. You’ll learn to harness callbacks in the Keras deep learning framework to make model training more efficient, and how to ensure consistent results in multiple training runs.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Mark Ryan
Mark Ryan has a Bachelor in Computer Science from the University of Waterloo and an MSc in Computer Science (with a specialty in Artificial Intelligence) from the University of Toronto. As a senior leader for IBM's Db2 relational database, he was immersed in structured, tabular data. Mark is currently a Data Science Manager at Intact Insurance. In addition to deep learning, Mark's interests include chatbots and transformer-based language models.

prerequisites

This liveProject is for intermediate data scientists looking to expand their skill set with applied deep learning capabilities. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Beginner pandas
  • Beginner scikit-learn
  • Beginner/Intermediate Keras
  • Familiarity with Google Colab
  • Familiarity with Jupyter Notebook
TECHNIQUES
  • Deep learning model layer assembly
  • Deep learning model training
  • Trained deep learning model one-off scoring

features

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book resources
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  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • renews monthly, pause or cancel renewal anytime
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