人工智能资料库:第23辑(20170201)
人工智能资料库:第23辑(20170201)
AllenOR灵感 发表于6个月前
人工智能资料库:第23辑(20170201)
  • 发表于 6个月前
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  1. 【视频】A Path to AI | Yann LeCun

简介:

Yann LeCun gives an overview of AI and outlines a path toward more general and complete AI at the January 2017 Asilomar conference organized by the Future of Life Institute.

The Beneficial AI 2017 Conference: In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients and followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial.

原文链接:https://www.youtube.com/watch?v=bub58oYJTm0


2.【代码】line drawing colorization using chainer

简介:


Paints Chainer is line drawing colorizer using chainer. Using CNN, you can colorize your scketch automatically / semi-automatically .

原文链接:https://github.com/pfnet/PaintsChainer

demo链接:http://paintschainer.preferred.tech/


3.【教程】Ultimate Guide to Understand & Implement Natural Language Processing (with codes in Python)

简介:

According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured in nature.

Few notorious examples include – tweets / posts on social media, user to user chat conversations, news, blogs and articles, product or services reviews and patient records in the healthcare sector. A few more recent ones includes chatbots and other voice driven bots.

Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system.

In order to produce significant and actionable insights from text data, it is important to get acquainted with the techniques and principles of Natural Language Processing (NLP).

So, if you plan to create chatbots this year, or you want to use the power of unstructured text, this guide is the right starting point. This guide unearths the concepts of natural language processing, its techniques and implementation. The aim of the article is to teach the concepts of natural language processing and apply it on real data set.

原文链接:https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/


4.【demo】Deep Learning and Sentiment Analysis

简介:

This repository contains code in Torch 7 for text classification from character-level using convolutional networks. It can be used to reproduce the results in the following article:

Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)

Note: An early version of this work entitled “Text Understanding from Scratch” was posted in Feb 2015 as arXiv:1502.01710. The present paper above has considerably more experimental results and a rewritten introduction.

原文链接:http://osdcwebappdeeplearning.azurewebsites.net/


5.【博客】
Learning Policies For Learning Policies — Meta Reinforcement Learning (RL²) in Tensorflow

简介:

Reinforcement Learning provides a framework for training agents to solve problems in the world. One of the limitations of these agents however is their inflexibility once trained. They are able to learn a policy to solve a specific problem (formalized as an MDP), but that learned policy is often useless in new problems, even relatively similar ones.

原文链接:https://hackernoon.com/learning-policies-for-learning-policies-meta-reinforcement-learning-rl%C2%B2-in-tensorflow-b15b592a2ddf#.r75ht8t0s


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