Kurs
Advanced Deep Learning with Keras
Orta SeviyeBeceri Seviyesi
Güncel 11.2024Kursa Ücretsiz Başlayın
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PythonArtificial Intelligence4 sa13 video46 Egzersiz3,950 XP34,706Başarı Belgesi
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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.
Önkoşullar
Introduction to Deep Learning with Keras1
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.
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.
Advanced Deep Learning with Keras
Kurs Tamamlandı
Başarı Belgesi Kazanın
Bu kimlik bilgisini LinkedIn profilinize, özgeçmişinize veya CV'nize ekleyinSosyal medyada ve performans incelemenizde paylaşın
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Şimdi KaydolunBugün 19 milyondan fazla öğrenciye katılın ve Advanced Deep Learning with Keras eğitimine başlayın!
Ücretsiz Hesabınızı Oluşturun
veya
Devam ederek Kullanım Şartlarımızı, Gizlilik Politikamızı ve verilerinizin ABD’de saklandığını kabul etmiş olursunuz.