Back to Custom Models, Layers, and Loss Functions with TensorFlow
DeepLearning.AI

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.

Status: Keras (Neural Network Library)
Status: Model Evaluation
IntermediateCourse31 hours

Featured reviews

RA

5.0Reviewed Jan 7, 2021

I started this course with the intention of learning the syntax needed to implement VAEs. This course satisfied that requirement perfectly! Thank you :)

QT

5.0Reviewed Aug 30, 2021

extremely detailed course on how to use API functions, I found that the weeks in which week 1 is the most difficult with homework the following weeks they are too simple

MS

5.0Reviewed Aug 23, 2023

Very interesting course! Here, I learned a lot of new things related to TensorFlow. The explanation of the material is easy to understand, and the exercises are also quite challenging.

DG

5.0Reviewed Nov 24, 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.

AM

5.0Reviewed Jul 30, 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.

PD

5.0Reviewed Jan 15, 2021

Wow! What a course it is! Amazing. Thanks to DeepLearningAi and Laurence for this course. But the mentors should be more active in the discussion forum. Not everyone is not comfortable with slack.

FG

5.0Reviewed Nov 11, 2022

Very interesting and well-explained course. Laurence is an amazing instructor and makes everything easier to understand and master. I definitely will be recommending this course to my colleagues!

AF

5.0Reviewed Oct 14, 2022

A​mazing course. I had done the deep learning specialization before this to understand principles, so this course helped me implement those principles in tensorflow

LS

5.0Reviewed Apr 11, 2021

It was a very good course, very clearly explained. The rhythm was not so high and I enjoyed the chance to go step by step going in deep in important concepts.

SS

5.0Reviewed Feb 15, 2022

1​. Course labs are well designed2​. Material covers real world problems3​. Course is well designed 4​. Instructors are very good in the subject5​. Teaching method is good

MS

5.0Reviewed Nov 25, 2020

Really great course, it teaches you all about the TF API and how to customize it for your needs, i thought only pytorch can make that as it's really pythonic, but i am a nieve noob what can i say.

DS

5.0Reviewed Mar 30, 2021

I was looking for a course about this specific topics. Previous NN courses were cool, but I think they kep short on making more complex Architechtures, which is perfectly addressed in this course.

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