Deep Learning Prediction

Deploy a Predictor on Android you own this product

This project is part of the liveProject series Deep Learning for Basketball Scores Prediction
prerequisites
basics of Python, TensorFlow, Keras, and Android development
skills learned
Converting a model to Android use • generating JavaScript usable data sets • simple Android development
Evan Hennis
1 week · 4-6 hours per week · INTERMEDIATE

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Look inside
In this liveProject, you’ll create an Android application that can run a pretrained basketball predictor deep learning model for the easy use of your client. Your challenges will include converting the DataFrames into JavaScript arrays, converting your model into a TensorFlow Lite model, and finally packaging the model inside a working Android application.
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

Evan Hennis
Evan Hennis is a Google Developer Expert in Machine Learning and an international speaker. He has an undergraduate degree in Computer Science from Iowa State University and a Master's degree in Computer Science with a specialization in machine learning from Georgia Tech. He has spent over sixteen years in software development, working across multiple languages and domains.

prerequisites

This project is for intermediate Python programmers or Android developers trying to add some data science to their applications looking to enhance their data science skills with deep learning techniques. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Python
  • Basics of pandas
  • Basics of Google Colab
  • Basics of TensorFlow and Keras
  • TECHNIQUES
    • Basics of machine learning
    • Basics of Android development

you will learn

In this liveProject series, you’ll learn all the steps needed to deliver a working machine learning project to a client.

  • Converting a model for use in an Android application with Python and TensorFlow
  • Converting a trained network to using TensorFlow Lite
  • Generate usable datasets accessible in JavaScript using pandas and NumPy
  • Create a mobile application with Java and Android Studio

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.

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