- A Layman’s Guide to Data Science. Part 3: Data Science Workflow - Jul 6, 2020.
Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.
- A Layman’s Guide to Data Science. Part 2: How to Build a Data Project - Apr 2, 2020.
As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.
- Can Edge Analytics Become a Game Changer? - Feb 28, 2020.
Edge analytics is considered to be the future of sensor handling, and this article discusses its benefits and architecture of modern edge devices, gateways, and sensors. Deep Learning for edge analytics is also considered along with a review of experiments in human and chess figure detection using edge devices.
- A Comprehensive Guide to Natural Language Generation - Jan 7, 2020.
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.
- Data Cleaning and Preprocessing for Beginners - Nov 7, 2019.
Careful preprocessing of data for your machine learning project is crucial. This overview describes the process of data cleaning and dealing with noise and missing data.
- Anomaly Detection, A Key Task for AI and Machine Learning, Explained - Oct 21, 2019.
One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert.
- How to Become a (Good) Data Scientist – Beginner Guide - Oct 16, 2019.
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.
- Understanding Tensor Processing Units - Jul 30, 2019.
The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.
- NLP vs. NLU: from Understanding a Language to Its Processing - Jul 3, 2019.
As AI progresses and the technology becomes more sophisticated, we expect existing techniques to evolve. With these changes, will the well-founded natural language processing give way to natural language understanding? Or, are the two concepts subtly distinct to hold their own niche in AI?
- NLP and Computer Vision Integrated - Jun 5, 2019.
Computer vision and NLP developed as separate fields, and researchers are now combining these tasks to solve long-standing problems across multiple disciplines.
- Machine Learning in Agriculture: Applications and Techniques - May 14, 2019.
Machine Learning has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data intensive processes in agricultural operational environments.
- Best Data Visualization Techniques for small and large data - Apr 17, 2019.
Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.
- Towards Automatic Text Summarization: Extractive Methods - Mar 13, 2019.
The basic idea looks simple: find the gist, cut off all opinions and detail, and write a couple of perfect sentences, the task inevitably ended up in toil and turmoil. Here is a short overview of traditional approaches that have beaten a path to advanced deep learning techniques.
- Interspeech 2018: Highlights for Data Scientists - Dec 24, 2018.
Key highlights from the Interspeech conference, with topics covering end-to-end models for automatic speech recognition, information theory approach to deep learning, speech processing and education, and more.
- Word Vectors in Natural Language Processing: Global Vectors (GloVe) - Aug 29, 2018.
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.
- NLP in Online Courses: an Overview - May 28, 2018.
This article examines several Natural Language Processing (NLP) courses across a variety of online sources and programming languages.