AutoKeras

Basic Deep Learning you own this product

This project is part of the liveProject series Image Classification with AutoKeras
prerequisites
basic Python, pandas, Matplotlib, NumPy, SciPy, scikit-learn, and deep learning/Keras
skills learned
constructing deep learning models for image classification • search the hyperparameters of deep models with grid search and random search
Xiaotian Han
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside

In this liveProject, you’ll learn and apply some of the basics of deep learning in order to build the foundations of an AutoML image classifier. You’ll discover the basic deep learning models for image classification, and then experiment with tuning the hyperparameters of a convolutional neural network to improve your results.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Xiaotian Han
Xiaotian Han is a PhD student at Texas A&M University, where he works in the field of data mining and automated machine learning. He has published several research papers related to data mining and automated machine learning and participated in an open-source package, AutoRec, which is highly based on the AutoKeras.

prerequisites

This liveProject is for Python programmers who know the basics of deep learning. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Basic Python
  • Basic pandas
  • Basic Matplotlib
  • Basic NumPy
  • Basic SciPy
  • Basic scikit-learn
TECHNIQUES
  • Basic deep learning/Keras

you will learn

This liveProject introduces the basics of traditional deep learning, as part of a journey into utilizing automated machine learning practices.


  • Constructing deep learning models for image classification
  • Search the hyperparameters of deep models with grid search and random search

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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.

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