AWS Machine Learning Blog

APRIL 5, 2023

Data overview and preparation You can use a SageMaker Studio notebook with a Python 3 (Data Science) kernel to run the sample code. 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. path local_data_root = f'.

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Towards AI

APRIL 23, 2025

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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K-nearest Neighbors Database ETL Machine Learning 58

Mlearning.ai

MAY 21, 2023

Youtube Comments Extraction and Sentiment Analysis Flask App Hey, guys in this blog we will implement Youtube Comments Extraction and Sentiment Analysis in Python using Flask. This is one of the best Machine learning projects with source code in Python. Check out the demo here… [link] 21. This is going to be a very short blog.

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Machine Learning Machine Learning Python K-nearest Neighbors 52

Mlearning.ai

MAY 23, 2023

This is one of the best Machine Learning Projects for final year in Python. Youtube Comments Extraction and Sentiment Analysis Flask App Hey, guys in this blog we will implement Youtube Comments Extraction and Sentiment Analysis in Python using Flask. Check out the demo here… [link] 21. This is going to be a very short blog.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

AWS Machine Learning Blog

SEPTEMBER 25, 2024

Testing the Streamlit app in a SageMaker environment is intended for a temporary demo. Choose the default Python 3 kernel and Data Science 3.0 find_similar_items performs semantic search using the k-nearest neighbors (kNN) algorithm on the input image prompt. An end-to-end demo is shown below.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Another driver behind RAG’s popularity is its ease of implementation and the existence of mature vector search solutions, such as those offered by Amazon Kendra (see Amazon Kendra launches Retrieval API ) and Amazon OpenSearch Service (see k-Nearest Neighbor (k-NN) search in Amazon OpenSearch Service ), among others.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Generate and run data transformation Python code. We tried different methods, including k-nearest neighbor (k-NN) search of vector embeddings, BM25 with synonyms , and a hybrid of both across fields including API routes, descriptions, and hypothetical questions. Generate and invoke private API calls.

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