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Learner Reviews & Feedback for Probabilistic Deep Learning with TensorFlow 2 by Imperial College London

4.9
stars
15 ratings
4 reviews

About the Course

Welcome to this course on Probabilistic Deep Learning with TensorFlow! This course builds on the foundational concepts and skills for TensorFlow taught in the first two courses in this specialisation, and focuses on the probabilistic approach to deep learning. This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders. You will learn how to develop models for uncertainty quantification, as well as generative models that can create new samples similar to those in the dataset, such as images of celebrity faces. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a variational autoencoder algorithm to produce a generative model of a synthetic image dataset that you will create yourself. This course follows on from the previous two courses in the specialisation, Getting Started with TensorFlow 2 and Customising Your Models with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is a solid foundation in probability and statistics. In particular, it is assumed that you are familiar with standard probability distributions, probability density functions, and concepts such as maximum likelihood estimation, change of variables formula for random variables, and the evidence lower bound (ELBO) used in variational inference....

Top reviews

FK
Dec 28, 2020

Very good. Liked this course a lot, even though I recognize I should have had a better a background before taking it.

MK
Nov 19, 2020

Really good course touching some really recent research in deep learning.

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1 - 4 of 4 Reviews for Probabilistic Deep Learning with TensorFlow 2

By Carl T

Oct 26, 2020

Taught concepts were highly interesting and the video lectures were really great. They were very clear and pedagogical. Most TAs had pretty good walkthroughs too.

Why I cannot rate a perfect 5/5 is because many of the lab assignments contained warts and/or misleading instructions that need polish. However, as the course organizer has been frequenting the discussion forum and been very quick to adjust instructions when pointed out by participants, I'm sure the course has already improved greatly. My experience was a bit confused at times however, and I wasn't sure if I had done mistakes or if the automatic judge has bugs, at times.

Overall, I highly recommend this course to anyone interested in learning a modern API for generative modelling. I'm sure I'll come back to the more difficult concepts like the autoregressive trick in normalizing flows, and the change of variable formula, and hopefully having done this course will make those concepts a bit more familiar next time around for me.

Thanks for providing this course material! The future is truly now for online education. :D

By Asad K

Dec 10, 2020

This was a wonderful introduction, not only to TensorFlow probability but also to the basic fundamentals of probabilistic deep learning. This is also, to the best of my knowledge, the only MOOC that gives a somewhat holistic overview of this domain. I'd totally recommend this course to any beginner looking to quickly pick up tf probability.

By Fabio K

Dec 29, 2020

Very good. Liked this course a lot, even though I recognize I should have had a better a background before taking it.

By Marios K

Nov 20, 2020

Really good course touching some really recent research in deep learning.