Interactive Course

Advanced Deep Learning with Keras in Python

Build multiple-input and multiple-output deep learning models using Keras.

  • 4 hours
  • 13 Videos
  • 46 Exercises
  • 2,893 Participants
  • 3,950 XP

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Course Description

This course shows you how to solve a variety of problems using the versatile Keras functional API. You will start with simple, multi-layer dense networks (also known as multi-layer perceptrons), and continue on to more complicated architectures. The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model. We will also cover advanced topics such as category embeddings and multiple-output networks. If you've ever wanted to train a network that does both classification and regression, then this course is for you!

  1. Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers

    In this chapter, you will build two-input networks that use categorical embeddings to represent high-cardinality data, shared layers to specify re-usable building blocks, and merge layers to join multiple inputs to a single output. By the end of this chapter, you will have the foundational building blocks for designing neural networks with complex data flows.

  2. Multiple Outputs

    In this chapter, you will build neural networks with multiple outputs, which can be used to solve regression problems with multiple targets. You will also build a model that solves a regression problem and a classification problem simultaneously.

  1. 1

    The Keras Functional API

    Free

    In this chapter, you'll become familiar with the basics of the Keras functional API. You'll build a simple functional network using functional building blocks, fit it to data, and make predictions.

  2. Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers

    In this chapter, you will build two-input networks that use categorical embeddings to represent high-cardinality data, shared layers to specify re-usable building blocks, and merge layers to join multiple inputs to a single output. By the end of this chapter, you will have the foundational building blocks for designing neural networks with complex data flows.

  3. Multiple Inputs: 3 Inputs (and Beyond!)

    In this chapter, you will extend your 2-input model to 3 inputs, and learn how to use Keras' summary and plot functions to understand the parameters and topology of your neural networks. By the end of the chapter, you will understand how to extend a 2-input model to 3 inputs and beyond.

  4. Multiple Outputs

    In this chapter, you will build neural networks with multiple outputs, which can be used to solve regression problems with multiple targets. You will also build a model that solves a regression problem and a classification problem simultaneously.

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Zachary Deane-Mayer
Zachary Deane-Mayer

Automation First Data Scientist at DataRobot

Zach is a Data Scientist at DataRobot and co-author of the caret R package. He's fascinated by predicting the future and spends his free time competing in predictive modeling competitions. He's currently one of top 500 data scientists on Kaggle and took 9th place in the Heritage Health Prize as part of the Analytics Inside team.

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Collaborators
  • Sumedh Panchadhar

    Sumedh Panchadhar

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