Machine learning is a field of artificial intelligence (AI) that is concerned with learning from data. Machine learning has three components:
Supervised learning: Fitting predictive models using data for which outcomes are available.
Unsupervised learning: Transforming and partitioning data where outcomes are not available.
Reinforcement learning: on-line learning in environments where not all events are observable. Reinforcement learning is frequently applied in robotics.
Posts on machine learning
In the following posts, machine learning is applied to solve problems using R.
For data scientists and data engineers, d6tflow is a python library which makes building complex data science workflows easy, fast and intuitive. It is primarily designed for data scientists to build better models faster. For data engineers, it can also be a lightweight alternative and help productionize data science models faster. Unlike other data pipeline/workflow solutions, d6tflow focuses on managing data science research workflows instead of managing production data pipelines.
Jina is a MLOps framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer.
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Recommending related products — say, a phone case to go along with a new phone — is a fundamental capability of e-commerce sites, one that saves customers time and leads to more satisfying shopping experiences.
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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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Once we go from training one model to training hundreds of different models with different hyperparameters, we need to start organizing. We’re going to break down our organization into three pieces: experiment tracking, hyperparameter search, and configuration setup.
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Transfer learning refers to the process of pre-training a flexible model on a large dataset and using it later on other data with little to no training. It is one of the most outstanding 🚀 achievements in Machine Learning 🧠 and has many practical applications.
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The Responsible Machine Learning Principles are a practical framework put together by domain experts. Their purpose is to provide guidance for technologists to develop machine learning systems responsibly. Link
Natural Language Processing remains one of the hottest topics of 2022. By using GitHub stars (albeit certainly not the only measure) as a proxy for popularity, we took a look at what NLP projects are getting the most traction so far this year, just as we recently did with machine learning projects. It’s a list with some familiar names but there are plenty of surprises also!
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A model registry is a central repository that is used to version control Machine Learning (ML) models. It simply tracks the models while they move between training, production, monitoring, and deployment.
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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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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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