There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc.
Contextual bandit models are a popular approach for personalizing the user experience by recommending relevant products. However, these models become challenging to train and evaluate when the number of actions, or products, in the recommendation pool become large. In this blog post, we will explore the difficulties associated with using contextual bandit models in large action spaces and propose potential solutions to overcome these challenges. One of these solutions was recently launched into production at Instacart.
We want to present Cleora – an open-source tool for creating compact representation of the behavior of your client. Cleora uses graph theory to transform streams of event data into embedding. It is suitable as an input for training models like churn, propensity and recommender systems. This is a talk useful for anyone who wishes to learn how to work with event data of clients and how to model client’s behavior.
In this report, we will walk you through how you can leverage PyTorch Geometric along with Weights & Biases to analyse the Amazon products graph and recommend products from the graph.
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This repository aims to simplify this by mapping the ecosystem of guidelines, principles, codes of ethics, standards and regulation being put in place around artificial intelligence.
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Multi-armed bandits have now been studied for nearly a century. While research in the beginning was quite meandering, there is now a large community publishing hundreds of articles every year. Bandit algorithms are also finding their way into practical applications in industry, especially in on-line platforms where data is readily available and automation is the only way to scale.
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This document is intended to help those with a basic knowledge of machine learning get the benefit of Google’s best practices in machine learning. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine-learned model, then you have the necessary background to read this document.
Imagine the following scenario: You have a brilliant idea for a new AI project. To make it happen, you need to convince management to fund your idea. You need to pitch your AI project idea to stakeholders and management. Yuck.
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Learn advanced computer vision using Python in this full course. You will learn state of the art computer vision techniques by building five projects with libraries such as OpenCV and Mediapipe. If you are a beginner, don’t be afraid of the term advance. Even though the concepts are advanced,
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