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Advanced Deep Learning with Keras

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更新日 2024/11

Learn how to develop deep learning models with Keras.

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PythonArtificial Intelligence4時間13 ビデオ46 演習3,950 XP34,844達成証明書

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コース説明

Keras functional API

In this course, you will learn how to solve complex problems using the Keras functional API.

Beginning with an introduction, you will build simple functional networks, fit them to data, and make predictions. You will also learn how to construct models with multiple inputs and a single output and share weights between layers.

Multiple-input networks

As you progress, explore building two-input networks using categorical embeddings, shared layers, and merge layers. These are the foundational building blocks for designing neural networks with complex data flows.

It extends these concepts to models with three or more inputs, helping you understand the parameters and topology of your neural networks using Keras' summary and plot functions.

Multiple-output networks

In the final interactive exercises, you'll work with multiple-output networks, which can solve regression problems with multiple targets and even handle both regression and classification tasks simultaneously.

By the end of the course, you'll have practical experience with advanced deep learning techniques to advance your career as a data scientist, including evaluating your models on new data using multiple metrics.

前提条件

Introduction to Deep Learning with Keras

1

The Keras Functional API

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.

Keras input and dense layers

Input layers

Dense layers

Output layers

Build and compile a model

Build a model

Compile a model

Visualize a model

Fit and evaluate a model

Fit the model to the tournament basketball data

Evaluate the model on a test set

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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.

Category embeddings

Define team lookup

Define team model

Shared layers

Defining two inputs

Lookup both inputs in the same model

Merge layers

Output layer using shared layer

Model using two inputs and one output

Predict from your model

Fit the model to the regular season training data

Evaluate the model on the tournament test data

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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.

Three-input models

Make an input layer for home vs. away

Make a model and compile it

Fit the model and evaluate

Summarizing and plotting models

Model summaries

Plotting models

Stacking models

Add the model predictions to the tournament data

Create an input layer with multiple columns

Fit the model

Evaluate the model

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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.

Two-output models

Simple two-output model

Fit a model with two outputs

Inspect the model (I)

Evaluate the model

Single model for classification and regression

Classification and regression in one model

Compile and fit the model

Inspect the model (II)

Evaluate on new data with two metrics

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Advanced Deep Learning with Keras

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