All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them.

All of a sudden every one is talking about them – irrespective of whether they understand the differences or not! Whether you have been actively following data science or not – you would have heard these terms. If you have often wondered to yourself what is the difference between machine learning and deep learning, read on to find out a detailed comparison in simple layman language. I have explained each of these term in detail. Then I have gone ahead to compare both of them and explained where we can use them. Let us start with the basics – What is Machine Learning and What is Deep Learning.

Let's see some simple example which helps you to illustrate the reinforcement learning mechanism. Consider the scenario of teaching new tricks to your cat. There are three approaches to implement a Reinforcement Learning algorithm. In a value-based Reinforcement Learning method, you should try to maximize a value function V(s). In this method, the agent is expecting a long-term return of the current states under policy π.

Niu, Gang, Plessis, Marthinus Christoffel du, Sakai, Tomoya, Ma, Yao, Sugiyama, Masashi

In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning based on the upper bounds on estimation errors. We find simple conditions when PU and NU learning are likely to outperform PN learning, and we prove that, in terms of the upper bounds, either PU or NU learning (depending on the class-prior probability and the sizes of P and N data) given infinite U data will improve on PN learning.