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This course is part of TensorFlow: Advanced Techniques Specialization
Instructors: Laurence Moroney
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41,507 already enrolled
(1,085 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
(1,085 reviews)
Recommended experience
Intermediate level
Basic calculus, linear algebra, stats
Knowledge of AI, deep learning
Experience with Python, TF/Keras/PyTorch framework, decorator, context manager
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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.
Compare how the Functional API differs from the Sequential API, and see how the Functional API gives you additional flexibility in designing models. Practice using the functional API and build a Siamese network!
11 videos7 readings1 assignment1 programming assignment1 app item3 ungraded labs
Loss functions help measure how well a model is doing, and are used to help a neural network learn from the training data. Learn how to build custom loss functions, including the contrastive loss function that is used in a Siamese network.
9 videos3 readings1 assignment1 programming assignment3 ungraded labs
Custom layers give you the flexibility to implement models that use non-standard layers. Practice building off of existing standard layers to create custom layers for your models.
10 videos1 reading1 assignment1 programming assignment3 ungraded labs
You can build off of existing models to add custom functionality. This week, extend the TensorFlow Model Class to build a ResNet model!
7 videos3 readings1 assignment1 programming assignment2 ungraded labs
Custom callbacks allow you to customize what your model outputs or how it behaves during training. This week, implement a custom callback to stop training once the callback detects overfitting.
3 videos4 readings2 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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Reviewed on Jul 29, 2022
It was a very useful course. Now I can build deep learning models with my desired architecture. I am also able to understand the implementation method of famous models like VGG-16.
Reviewed on Aug 9, 2021
This course consists of good explanations and coding exercises. followed by not overly demanding practical assignments. It is informative and opens the world of Tensorflow models customization.
Reviewed on Nov 23, 2020
Such an awesome course. The examples given are just to the point. Can't thank enough Coursera for providing such a lovely platform and Laurence, what an amazing instructor.
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