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AI for Business

GPT-3: What is GPT-3 and what can it do for your business?
GPT-3 is a popular AI tool that everyone is raving about. What exactly is GPT-3 and what does it mean for your AI problems?
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What went wrong with Tay, the Twitter bot that turned racist?
Why Tay the Twitter bot from 2016 failed and what we can learn from it. How can we generalize this to ML development best practices?
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3 Strategic Mistakes Leaders Can Easily Avoid When Thinking About AI Integration
Rushing through AI integration can result in unintended failures. Learn 3 AI integration mistakes leaders can easily avoid to limit confusion, reduce waste, and truly reap the…

How Sentiment Analysis Keeps Your Brand in Check (and How to Get Started)
We’ve all heard of sentiment analysis, but what exactly is it and what can it do for your brand, your business, and how can you get started…

Even if AI is Your Goal, Why Starting Without AI Improves Outcomes
In this article, you’ll learn why it’s sometimes wiser to start without AI, even if your goal is to integrate AI into products, services, and workflows. You’ll…

15 Common AI Problem Types
One of the problems business leaders face in communicating with their technical counterparts is trying to describe their AI problem. To simplify some of the communication, here…
Technical Deep Dive for Practitioners

A Gentle Introduction to Deep Neural Networks with Python
This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which…

FastText vs. Word2vec: A Quick Comparison
One of the questions that often comes up is what’s the difference between fastText and Word2Vec? Aren’t they both the same? Yes and no. They are conceptually the same, but there is a minor difference—fastText operates at a character…

Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI
Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts…

HashingVectorizer vs. CountVectorizer
Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use…

10+ Examples for Using CountVectorizer
Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a…

Build Your First Text Classifier in Python with Logistic Regression
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear…