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 . . .
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 . . .
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 . . .
Visual Evaluation and Parameter Tuning
Welcome back! In this final installment of Visual Diagnostics for More Informed Machine Learning, we'll close the loop on visualization tools for navigating the different phases of the machine learning workflow. Recall that we are framing the workflow in terms of . . .
Demystifying Model Selection
Note: Before starting Part 2, be sure to read Part 1!
When it comes to machine learning, ultimately the most important picture to have is the big picture. Discussions of (i.e. arguments about) machine learning are usually about which model is the best. Whether it's logistic regression, random forests, Bayesian methods, support . . .
How could they see anything but the shadows if they were never allowed to move their heads?
— Plato The Allegory of the Cave
Python and high level libraries like Scikit-learn, TensorFlow, NLTK, PyBrain, Theano, and MLPY have made machine learning accessible to a broad programming community that might never . . .