Into the Unknown
Probabilistic Deep Learning with TensorFlow 2Imperial College London The focus of this course is the TensorFlow Probability library. Spoiler alert! Probability distributions are important factors you need to consider. From now on, building model is not only as simple as adding layers and squeezing your GPU to calculate various weights. This is challenging course. From…
The Variational Autoencoder (VAE) is an algorithm for inference and learning in a latent variable generative model. In it’s simplest form, it’s an unsupervised learning algorithm and like normalizing flows, the generative model can be used to create new examples similar to the data set. However, unlike normalizing flows, the generative model is not invertible…
Generative models are a kind of statistical model that aims to learn the underlying data distribution itself. If a generative model is able to capture the underlying distribution of the data well, then it’s able to produce new instances that could plausibly have come from the same dataset. You could use for anomaly detection, telling you whether a given instance…
Unfortunately, deep learning models aren’t always accurate, especially when asked to make predictions on new data points that are dissimilar to the data that they were trained on. The insight here is that it’s important to models to be able to assign higher levels of uncertainty to incorrect predictions. We want our deep learning models to know what they don’t know. The probabilistic…
We’ll be making extensive use of the TensorFlow Probability library to help us develop probabilistic deep learning models. The distribution objects from the library are the vital building blocks because they capture the essential operations on probability distributions. We are going to use them when building probabilistic deep learning models in TensorFlow. Univariate Distributions Within the tfp…