In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication.

In machine learning (ML) methods, computers perform a task by relying on statistics, patterns, and inference instead of explicit instructions.

The Department of Computer Science has a vacancy for a PhD position within machine learning related to digital rocks.

Machine vision has an impressive record. It has the superhuman ability to recognize people, faces and objects. It can even recognize many different kinds of actions, albeit not quite as well as humans just yet.

GPT-2 generates text that is far more realistic than any text generation system before it. OpenAI was so shocked by the quality of the output that they decided that the full GPT-2 model was too dangerous to release because it could be used to create endless amounts of fake news that could fool the public or clog up search engines like Google. How easy it is for an average person to generate fake news that could trick a real person and how good are the results? Let's explore how a system like this could work and how much of a threat it is. Let's try to build a newspaper populated with fake, computer generated news: To populate News You Can't Use, we'll create a Python script that can'clone' a news site like the New York Times and generate artificial news stories on the same topics.

Has google learned it from the machine or is it the other way round? Google is to larry, What Machine learning is to future! The biggest buzzword of all times in the overall technology market is machine learning. With no surprise, Google has mastered it, and with all its applications it has made our life easy! So, are you questioning yourself of what all achievements do Google possess? Don't take that pain, we will decode that in this article.

Abstract: Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following this result, there have been several papers showing reasonable performances under a variety of environments and batch settings. In this paper, we benchmark the performance of recent off- policy and batch reinforcement learning algorithms under unified settings on the Atari domain, with data generated by a single partially-trained behavioral policy. We find that under these conditions, many of these algorithms underperform DQN trained online with the same amount of data, as well as the partially-trained behavioral policy. To introduce a strong baseline, we adapt the Batch-Constrained Q-learning algorithm to a discrete-action setting, and show it outperforms all existing algorithms at this task.

Intuitively, this layer learns to represent concepts/words by their relation to other.