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Voltar para Probabilistic Deep Learning with TensorFlow 2

Comentários e feedback de alunos de Probabilistic Deep Learning with TensorFlow 2 da instituição Imperial College London

74 classificações
28 avaliações

Sobre o curso

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....

Melhores avaliações


16 de dez de 2021

This has been a great course! The lecture videos are clear, concise, and to the point. The assignments are perfectly structured and the feedbacks from assignments are super helpful.


26 de jul de 2021

A really valuable learning experience. With these courses, I now feel confident that I can apply the skills from the Deep Learning Specialization in a practical setting.

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26 — 31 de 31 Avaliações para o Probabilistic Deep Learning with TensorFlow 2

por Alexey

8 de out de 2021

I see not many courses that are so useful!

por Jeffrey B

6 de mar de 2021

Great course! Challenging yet rewarding.

por Pathirage D

17 de mai de 2021

well structured course content.

por Matthieu R

25 de fev de 2021

Great course :)


16 de fev de 2022

G​reat course, sometimes assignments are not so easy and slighty different from teached topics.

por Goh K L

31 de jan de 2022

Can be quite challenging at times, but it's good.