This post is by Phil, not Andrew.

Every cuisine, while sharing many common elements with others, uses a handful of ingredients that combine for unique flavors.

Nuts & Bolts of Reinforcement Learning: Model Based Planning using Dynamic Programming

Every cuisine, while sharing many common elements with others, uses a handful of ingredients that combine for unique flavors.

This post gives a short overview of our recent paper:

Extra Recipes for Encoding Categorical Predictors (embed)Factor predictors can be converted to one or more numeric representations using simple generalized linear models or nonlinear …

RadialGAN Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be rare. In this paper we propose a novel approach to the problem in which we use multiple GAN architectures to learn to translate from one dataset to another, thereby allowing us to effectively enlarge the target dataset, and therefore learn better predictive models than if we simply used the target dataset. We show the utility of such an approach, demonstrating that our method improves the prediction performance on the target domain over using just the target dataset and also show that our framework outperforms several other benchmarks on a collection of real-world medical datasets. …

Deep learning has provided extraordinary advances in problem spaces that are poorly solved by other approaches. This success is due to several key departures from traditional machine learning that allow it to excel when applied to unstructured data. Today, deep learning models can play games, detect cancer, talk to humans, and drive cars.

In this blog we will try to get the basic idea behind reinforcement learning and understand what is a multi arm bandit problem. We will also be trying to maximise CTR(click through rate) for advertisements for a advertising agency.Article includes:1. Basics of reinforcement learning2. Types of problems in reinforcement learning3. Understamding multi-arm bandit problem4. Basics of conditional probability and Thompson sampling5. Optimizing ads CTR using Thompson sampling in R

Matthew Poes writes: