Part 1: Skip-gram Feedforward
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out the other posts in the series so far:
- NLP Research Lab Part 1: Distributed Representations
- NLP Research Lab Part 2: Skip-Gram Architecture Overview
Let's continue our treatment of the . . .
Preparing Yourself to Become a Great Explorer
Exploratory data analysis (EDA) is an important pillar of data science, a critical step required to complete every project regardless of the domain or the type of data you are working with. It is exploratory analysis that gives us a sense of what additional work should be performed to quantify and extract insights from our data. It also . . .
Exceptions are a crucial part of higher level languages, and although exceptions might be frustrating when they occur, they are your friend. The alternative to an exception is a panic — an error in execution that at best simply makes the program die and at worst can cause a blue screen of death. Exceptions, on the other hand, are tools . . .
Posted in: python
An Overview and Tutorial
The amount of data generated each day from sources such as scientific experiments, cell phones, and smartwatches has been growing exponentially over the last several years. Not only are the number data sources increasing, but the data itself is also growing richer as the number of features in the data increases. Datasets with a large number . . .
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab. Make sure to check out NLP Research Lab Part 1: Distributed Representations.
Chances are, if you’ve been working in Natural Language Processing (NLP) or machine learning, you’ve heard of the class of approaches called . . .
How I Learned To Stop Worrying And Love Word Embeddings
Editor's Note: This post is part of a series based on the research conducted in District Data Labs' NLP Research Lab.
This post is about Distributed Representations, a concept that is foundational not only to the understanding of data processing in machine learning, but also to the understanding of information processing and storage . . .
Visualizing Text with Python
In this article, we explore two extremely powerful ways to visualize text: word bubbles and word networks. These two visualizations are replacing word clouds as the defacto text visualization of choice because they are simple to create, understandable, and provide deep and valuable at-a-glance insights. In this post, we will examine how to . . .