Probabilistic Deep Learning with TensorFlow 2
Master probabilistic deep learning using TensorFlow Probability, including Bayesian networks and generative models.
Master probabilistic deep learning using TensorFlow Probability, including Bayesian networks and generative models.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full TensorFlow 2 for Deep Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.7
(101 ratings)
13,576 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Implement probabilistic models using TensorFlow Probability
Develop Bayesian neural networks for uncertainty quantification
Create normalizing flows for complex distributions
Build variational autoencoders for generative modeling
Design robust models for real-world applications
Skills you'll gain
This course includes:
6.3 Hours PreRecorded video
4 assignments, 1 peer review
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FullTime access
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There are 5 modules in this course
This comprehensive course explores probabilistic approaches to deep learning using TensorFlow Probability. Students learn to develop models that quantify uncertainty in data and predictions, essential for applications in autonomous vehicles and medical diagnostics. The curriculum covers probability distributions, Bayesian neural networks, normalizing flows, and variational autoencoders, with hands-on projects including generative models for image synthesis.
TensorFlow Distributions
Module 1 · 12 Hours to complete
Probabilistic layers and Bayesian neural networks
Module 2 · 12 Hours to complete
Bijectors and normalising flows
Module 3 · 13 Hours to complete
Variational autoencoders
Module 4 · 12 Hours to complete
Capstone Project
Module 5 · 2 Hours to complete
Fee Structure
Instructor
Innovating Music with Machine Learning at Imperial College London
Kevin Webster is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London, where he earned his PhD in dynamical systems in 2003. His research focuses on integrating machine learning techniques with numerical approximation challenges in dynamical systems, as well as leveraging machine learning and deep learning models for music applications, including music generation and listening. Notably, he developed the core AI for the commercial music audio search engine Figaro.
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4.7 course rating
101 ratings
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