Remove en topics containers
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Turning YouTube Comments into Expert Movie Critiques with Python and AI: A Step-by-Step Guide”

Towards AI

This article aims to demonstrate how generative AI models can provide a fresh lens for aggregating and summarizing the collective voices on a single topic, like a movie. This is because some comments contain multiple distinct sentiments that can skew the clustering process if not properly segmented. In this project, we code in Python.

Python 85
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Enhancing customer experience: Streamlining orders with custom email notifications in IBM Cloud

IBM Journey to AI blog

Step 5: Create an IBM Cloud Event Notifications topic Next, you will define an IBM Cloud Event Notifications topic that will receive an event from IBM Cloud Secrets Manager. Click Topics. The Topic details panels will open. In the Topic details, enter the following: Enter the Name for your topic (e.g.,

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professionals

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Meet the winners of the Research Rovers: AI Research Assistants for NASA Challenge

DrivenData Labs

Team / participant Features Models Data sources NASAPalooza Paper search, paper recommendation, doc upload, paper summarization, chatbot, people search, keyword extraction, topic trends, dataset analysis GPT-3.5 bge-small-en-v1.5 bge-small-en-v1.5 bge-small-en-v1.5

AI 147
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Use Amazon SageMaker Studio to build a RAG question answering solution with Llama 2, LangChain, and Pinecone for fast experimentation

Flipboard

We use two AWS Media & Entertainment Blog posts as the sample external data, which we convert into embeddings with the BAAI/bge-small-en-v1.5 Deploy the BAAI/bge-small-en-v1.5 em_model_name = "BAAI/bge-small-en" em_model_path = f"./em-model" embeddings. embeddings model to a SageMaker real-time endpoint.

AWS 128
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Google Research, 2022 & beyond: Research community engagement

Google Research AI blog

Posted by Posted by Leslie Yeh, Director, University Relations (This is Part 9 in our series of posts covering different topical areas of research at Google. We partnered with ENS , a university in France, to help fund scholarships for students to train through research. You can find other posts in the series here.)

ML 72
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Gaining a sense of control over the COVID-19 pandemic | A Winner’s Interview with Daniel Wolffram

Kaggle

During my time as a student assistant, we’ve also consulted a company that works with a lot of text data — that’s where I gained my first experience in NLP and also came across the idea of finding similar documents with the help of a topic model. How did you get started competing on Kaggle? What machine learning methods did you use?

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Build financial search applications using the Amazon Bedrock Cohere multilingual embedding model

AWS Machine Learning Blog

This enables applications such as semantic search, Retrieval Augmented Generation (RAG), topic modeling, and text classification. Embeddings are generated by representational language models that translate text into numerical vectors and encode contextual information in a document.