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Custom Models, Layers, and Loss Functions with TensorFlow

In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. • Build off of existing standard layers to create custom layers for your models, customize a network layer with a lambda layer, understand the differences between them, learn what makes up a custom layer, and explore activation functions. • Build off of existing models to add custom functionality, learn how to define your own custom class instead of using the Functional or Sequential APIs, build models that can be inherited from the TensorFlow Model class, and build a residual network (ResNet) through defining a custom model class. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Created by DeepLearning.AI


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What you’ll learn


Through this learning resource you can gain confidence with the compentencies sought by organizations in 2021. The most in demand technique within the educational opportunity that is commonly requested by organizations is Data Modeling. The most relevant tool is Tensorflow.

Who will benefit?


Comparing material from this learning opportunity with nearly 10,000 data-related job descriptions, we find that those interested in Data Scientist roles have the most to gain.