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

Mlearning.ai

Source code projects provide valuable hands-on experience and allow you to understand the intricacies of machine learning algorithms, data preprocessing, model training, and evaluation. Wine Quality Prediction In this blog, we will build a simple Wine Quality Prediction model using the Random Forest algorithm.

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

Mlearning.ai

HOGs are great feature detectors and can also be used for object detection with SVM but due to many other State of the Art object detection algorithms like YOLO, and SSD , present out there, we don’t use HOGs much for object detection. Check out the demo here… [link] 21. Check out the demo here… [link] 24.

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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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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

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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