H2O.ai Blog
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How This AI Tool Breathes New Life Into Data Science
Ask any data scientist in your workplace. Any Data Science Supervised Learning ML/AI project will go through many steps and iterations before it can be put in production. Starting with the question of “Are we solving for a regression or classification problem?” Data Collection & Curation Are there Outliers? What is the Distribu...
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What does NVIDIA’s Rapids platform mean for the Data Science community?
Today NVIDIA announced the launch of the RAPIDS suite of software libraries to enables GPU acceleration for data science workflows and we’re excited to partner with NVIDIA to bring GPU accelerated open source technology for the machine learning and AI community. “Machine learning is transforming businesses and NVIDIA GPUs are speeding...
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Automatic Feature Engineering for Text Analytics - The Latest Addition to Our Kaggle Grandmasters' Recipes
According to Kaggle’s ‘The State of Machine Learning and Data Science ’ survey , text data is the second most used data type at work for data scientists. There are a lot of interesting text analytics applications like sentiment prediction, product categorization, document classification and so on. In the latest version (1.3) of our Driver...
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H2O4GPU now available in R
In September, H2O.ai released a new open source software project for GPU machine learning called H2O4GPU . The initial release (blog post here ) included a Python module with a scikit-learn compatible API, which allows it to be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. ...
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H2O4GPU Hands-On Lab (Video) + Updates
Aggregator DBSCAN Kalman Filters K-nearest neighbors Quantiles Sort If you’d like to learn more about H2O4GPU, I invite you to explore these helpful links: H2O4GPU README Open Source License (Apache 2.0) Happy Holidays! Rosalie ...
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H2O.ai Releases H2O4GPU, the Fastest Collection of GPU Algorithms on the Market, to Expedite Machine Learning in Python
H2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn Python API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algor...
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Driverless AI Blog
In today’s market, there aren’t enough data scientists to satisfy the growing demand for people in the field. With many companies moving towards automating processes across their businesses (everything from HR to Marketing), companies are forced to compete for the best data science talent to meet their needs. A report by McKinsey says th...
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Machine Learning on GPUs
With H2O GPU Edition, H2O.ai seeks to build the fastest artificial intelligence (AI) platform on GPUs. While deep learning has recently taken advantage of the tremendous performance boost provided by GPUs, many machine learning algorithms can benefit from the efficient fine-grained parallelism and high throughput of GPUs. Importantly, G...
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The Race for Intelligence: How AI is Eating Hardware - Towards an AI-defined hardware world
With the AI arms race reaching a fever pitch, every data-driven company is (or at least should be) evaluating its approach to AI as a means to make their owned datasets as powerful as they can possibly be. In fact, any business that’s not currently thinking about how AI can transform its operations risks falling behind its competitors and...
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H2O announces GPU Open Analytics Initiative with MapD & Continuum
H2O.ai, Continuum Analytics, and MapD Technologies have announced the formation of the GPU Open Analytics Initiative (GOAI) to create common data frameworks enabling developers and statistical researchers to accelerate data science on GPUs. GOAI will foster the development of a data science ecosystem on GPUs by allowing resident applicat...
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