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Advanced Deep Learning with Keras

4.6+
15 reviews
Intermediate

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

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4 Hours13 Videos46 Exercises
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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. 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.

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    Keras input and dense layers
    50 xp
    Input layers
    100 xp
    Dense layers
    100 xp
    Output layers
    100 xp
    Build and compile a model
    50 xp
    Build a model
    100 xp
    Compile a model
    100 xp
    Visualize a model
    100 xp
    Fit and evaluate a model
    50 xp
    Fit the model to the tournament basketball data
    100 xp
    Evaluate the model on a test set
    100 xp
  2. 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.

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  3. 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.

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  4. 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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In the following tracks

Keras Toolbox

Collaborators

Collaborator's avatar
Sumedh Panchadhar
Zachary Deane-Mayer HeadshotZachary Deane-Mayer

VP, Data Science at DataRobot

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Don’t just take our word for it

*4.6
from 15 reviews
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  • Hsing-chuan H.
    about 1 year

    A good quick overview of Advanced DL

  • Jorge C.
    about 1 year

    Great intro to Functional API

  • arwinder m.
    about 1 year

    Great course.

  • Jean-Pierre R.
    over 1 year

    I really did love this course, and it covers a good chunk of the advanced and applicable topics in deep learning. I wish it was longer and had more.

  • Aldo M.
    over 1 year

    cool

"A good quick overview of Advanced DL"

Hsing-chuan H.

"Great intro to Functional API"

Jorge C.

"Great course."

arwinder m.

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