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Semantris is a word association game powered by machine learning.
Semantris. Semantris is a word association game that uses this same technology. Each time you enter a clue, the AI looks at all the words in play and chooses the ones it thinks are most related.
Semantic Experiences lets you get hands-on with games and experiments that showcase advances in natural language understanding.
If you’re interested in making games that take advantage of Semantic ML, you should take a look at Semantris, which uses the same technology to detect word associations. You can experiment with the words it uses in the Semantic Reactor.
The Semantic Experiences on this site are all based on fully learned end-to-end models that can be used for a wide variety of natural language understanding applications. We're excited to share these models with the community to see what else can be built with them.
In this notebook, you'll learn how to fine-tune a pretrained vision model for Semantic Segmentation on a custom dataset in PyTorch. The idea is to add a randomly initialized segmentation head on top of a pre-trained encoder, and fine-tune the model altogether on a labeled dataset.
Basic Semantic Search. Language models give computers the ability to search by meaning and go beyond searching by matching keywords. This capability is called semantic search. In this notebook, we'll build a simple semantic search engine. The applications of semantic search go beyond building a web search engine.
The Rule Reranker. The Rule Reranker is an advanced feature of the Semantic Reactor that allows you to tweak the default behavior of the models by applying a set of rules.
Semantic segmentation is a type of computer vision task that involves assigning a class label such as person, bike, or background to each individual pixel of an image, effectively dividing the image into regions that correspond to different fobject classes or categories. KerasCV offers the DeepLabv3+ model developed by Google for semantic ...
Semantic Search. In this walkthrough we will see how to use Pinecone for semantic search. To begin we must install the required prerequisite libraries: [ ] !pip install -qU \. pinecone-client==3.1.0 \. pinecone-datasets==0.7.0 \. sentence-transformers==2.2.2 \. pinecone-notebooks==0.1.1.
