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Practical Tips and Tricks for Developers Building RAG Applications

Towards AI

To demonstrate this concept, I wrote a short demo in just ten lines of Python code using the k-nearest neighbors algorithm (KNN). argsort()] # Get the top k closest indices closest_k_indices = sorted_distances[:k, 1].astype(int)

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Implement unified text and image search with a CLIP model using Amazon SageMaker and Amazon OpenSearch Service

AWS Machine Learning Blog

For demo purposes, we use approximately 1,600 products. We use the first metadata file in this demo. We use a pretrained ResNet-50 (RN50) model in this demo. We only use the item images and item names in US English. For more details about this dataset, refer to the README. The dataset is hosted in a public S3 bucket.

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Build a multimodal social media content generator using Amazon Bedrock

AWS Machine Learning Blog

Testing the Streamlit app in a SageMaker environment is intended for a temporary demo. find_similar_items performs semantic search using the k-nearest neighbors (kNN) algorithm on the input image prompt. In the demo, we use the luxury brand and the fast fashion brand, each with its own preferences and guidelines.

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[Latest] 20+ Top Machine Learning Projects for final year

Mlearning.ai

How to perform Face Recognition using KNN So in this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. Check out the demo here… [link] 21. Check out the demo here… [link] 24. Check out the demo here… [link] 25. This is a simple project.

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[Latest] 20+ Top Machine Learning Projects with Source Code

Mlearning.ai

How to perform Face Recognition using KNN So in this blog, we will see how we can perform Face Recognition using KNN (K-Nearest Neighbors Algorithm) and Haar cascades. Check out the demo here… [link] 21. Check out the demo here… [link] 24. Check out the demo here… [link] 25. This is a simple project.

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How Foundation Models bolster programmatic labeling

Snorkel AI

So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space. You can register for a live demo of Snorkel Flow on February 16 which will feature the platform’s new FM capabilities. We’re essentially propagating the votes to nearby points that have abstains.

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How Foundation Models bolster programmatic labeling

Snorkel AI

So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space. You can register for a live demo of Snorkel Flow on February 16 which will feature the platform’s new FM capabilities. We’re essentially propagating the votes to nearby points that have abstains.