Bring new life to old photos by automatically colorizing them using the Algorithmia API. It's as easy as pasting in a URL above.
We're excited to introduce cloud hosted deep learning models on Algorithmia. The Colorful Image Colorization microservice is a computer vision algorithm trained on a million images from the Imagenet dataset.utpat porta
This microservice is an implementation of the Colorful Image Colorization algorithm created by Richard Zhang, Phillip Isola, and Alexei A. Efros..
Let us know what you think @Algorithmia or by email.
2,800 algorithmic microservices via an intuitive
infrastructure needed to run it at scale.
for public or private consumption. Every
each composable, interoperable, and secure.
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Text Analysis or Natural Language Processing (NLP) is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
Machine learning is a set of mathematical approaches to teaching computers to learn based on large quantities of data, instead of human step by step instruction. There are many categories of Machine Learning including Natural Language Processing, Computer Vision, Time Series, and more.
Computer Vision gives us the ability to teach a computer to make meaning of the physical world through vision. These tools allow us to develop applications that can make meaning from the input of cameras, photos, and videos to mind-bending degrees.
New to Deep Learning? Check our our gentle introduction to deep learning. More about deep learning: Why Deep Learning Matters and What’s Next for Artificial Intelligence Lessons Learned from Deploying Deep Learning at Scale Turn Your Deep Learning Model into a Serverless Microservice
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Phasellus luctus commodo ullamcorper a posuere rhoncus commodo elit. Aenean congue, risus utaliquam dapibus. Thanks!.
Phasellus luctus commodo ullamcorper a posuere rhoncus commodo elit. Aenean congue, risus utaliquam dapibus. Thanks!.