- 【博客】Natural Language Processing with Stanford CoreNLP
Today, we’ll be following up on our recent post on the Google Cloud Natural Language API. In this post, we’re going to take a second look at the service and compare it to the Stanford CoreNLP, a well-known suite for Natural Language Processing (NLP). We will walk you through how to get started using the Stanford CoreNLP, and then we’ll discuss the strengths and weaknesses of the two solutions.
2.【博客】Deep Learning, Artificial Intuition and the Quest for AGI
There is a renaissance happening in the field of artificial intelligence. For many long term practitioners in the fields, it is not too obvious. I wrote earlier about the push back that many are making against the developments of Deep Learning. Deep Learning is however an extremely radical departure from classical methods. One researcher who recognized that our classical approaches to Artificial General Intelligence (AGI) were all but broken is Monica Anderson.
3.【博客】Sentiment Analysis using Tf-Idf weighting: Python/Scikit-learn
Sentiment analysis in text mining is the process of categorizing opinions expressed in a piece of text. A basic form of such analysis would be to predict whether the opinion about something is positive or negative (polarity). There can be other forms of sentiment analysis or opinion mining like predicting rating scale on product’s review, predicting polarity on aspects of a product, detecting subjectivity and objectivity in sentences etc.
4.【代码】Data Processing Advanced: a course on scientific computing with Numpy/Scipy
Data Processing Advanced: a course on scientific computing with Numpy/Scipy
Researchers in many scientific and engineering fiels need to access, manipulate and analyze large amounts of data such as audio and video, images, text or physiological measurments. Data Science and Machine Learning are especially data-intensive fields. In this course you will learn how to leverage the Python tools for scientific computing to handle data in an efficient and effective manner. You will learn how to use numpy and scipy to load, transform and analyze data from a variety of domains.
5.【博客】4 Steps for Learning Deep Learning
Step 0: Learn Machine Learning Basics
Step 1: Dig into Deep Learning
Step 2: Pick a focus area and go deeper
Step 3: Build Something