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Transformer models: A guide to understanding different transformer architectures and their uses

Data Science Dojo

The very common model types under this category include encoder-only, decoder-only, and encoder-decoder transformers. Some common approaches that define classification under this category include Masked Language Models (MLMs), autoregressive models, and conditional transformers.

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Build brand loyalty by recommending actions to your users with Amazon Personalize Next Best Action

AWS Machine Learning Blog

In this post, we show you how to use the Next Best Action recipe to personalize action recommendations based on each user’s past interactions, needs, and behavior. We then see how Next Best Action suggests the optimal actions for each user based on their past interactions and preferences.

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Exploring Anthropic’s Claude 3 and its Position Among the Leading AI Chatbots

Data Science Dojo

AI chatbots are transforming the digital world with increased efficiency, personalized interaction, and useful data insights. While Open AI’s GPT and Google’s Gemini are already transforming modern business interactions, Anthropic AI recently launched its newest addition, Claude 3.

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Spotify’s Song Psychic can be your new fortuneteller

Dataconomy

It’s a fun and entertaining way to interact with music. So, while it may feel like interacting with an AI chatbot, the responses are more akin to playful coincidences rather than AI-driven insights. Select a category that interests you. Then, Spotify will respond with a song that matches your question.

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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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Mastering data visualization: Simplifying complex data for better insights

Data Science Dojo

Improved data exploration and analysis: It enables you to explore and analyze your data in a more intuitive and interactive way. Nonetheless, here are some most common visuals used in data science: Bar Charts: Bar charts are normally used to compare categorical data, such as the frequency or proportion of different categories.

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Exploration of Joint PMFs: Their Applications in Data Science (Part 1)

Towards AI

Joint PMFs help to understand how different variables interact with each other and identify patterns and correlations by providing the probabilities of combinations of variable outcomes. A joint probability table contains the following elements: Header Rows and Columns: These indicate the possible values or categories of each random variable.