Advanced Deep Learning with Keras
Learn how to develop deep learning models with Keras.
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Course Description
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.
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Keras Fundamentals
Go To Track- 1
The Keras Functional API
FreeIn 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 layers50 xpInput layers100 xpDense layers100 xpOutput layers100 xpBuild and compile a model50 xpBuild a model100 xpCompile a model100 xpVisualize a model100 xpFit and evaluate a model50 xpFit the model to the tournament basketball data100 xpEvaluate the model on a test set100 xp - 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 embeddings50 xpDefine team lookup100 xpDefine team model100 xpShared layers50 xpDefining two inputs100 xpLookup both inputs in the same model100 xpMerge layers50 xpOutput layer using shared layer100 xpModel using two inputs and one output100 xpPredict from your model50 xpFit the model to the regular season training data100 xpEvaluate the model on the tournament test data100 xp - 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 models50 xpMake an input layer for home vs. away100 xpMake a model and compile it100 xpFit the model and evaluate100 xpSummarizing and plotting models50 xpModel summaries100 xpPlotting models100 xpStacking models50 xpAdd the model predictions to the tournament data100 xpCreate an input layer with multiple columns100 xpFit the model100 xpEvaluate the model100 xp - 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 models50 xpSimple two-output model100 xpFit a model with two outputs100 xpInspect the model (I)100 xpEvaluate the model100 xpSingle model for classification and regression50 xpClassification and regression in one model100 xpCompile and fit the model100 xpInspect the model (II)100 xpEvaluate on new data with two metrics100 xpWrap-up50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Keras Fundamentals
Go To Trackcollaborators
prerequisites
Introduction to Deep Learning with Keras
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