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Advanced Deep Learning with Keras

Learn how to develop deep learning models with Keras.

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4 horas13 vídeos46 exercícios32.662 aprendizesTrophyDeclaração de Realização

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Descrição do Curso

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​.

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Nas seguintes faixas

Fundamentos de Keras

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  1. 1

    The Keras Functional API

    Gratuito

    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.

    Reproduzir Capítulo Agora
    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.

    Reproduzir Capítulo Agora
Para Empresas

GroupTreinar 2 ou mais pessoas?

Obtenha acesso à sua equipe à plataforma DataCamp completa, incluindo todos os recursos.

Nas seguintes faixas

Fundamentos de Keras

Ir para a trilha

conjuntos de dados

Basketball dataBasketball models

colaboradores

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

VP, Data Science at DataRobot

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