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Meet Quivr: An Open-Source Project Designed to Store and Retrieve Unstructured Information like a Second Brain

Flipboard

Researchers from many universities build open-source projects which contribute to the development of the Data Science domain. It is also called the second brain as it can store data that is not arranged according to a present data model or schema and, therefore, cannot be stored in a traditional relational database or RDBMS.

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What is TensorFlow? Core Components & Benefits

Pickl AI

It is critical in powering modern AI systems, from image recognition to natural language processing. TensorFlow enables developers and Data Scientists to build, train, and deploy Machine Learning applications quickly and efficiently. At its core, TensorFlow is a library for numerical computation using data flow graphs.

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Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

AWS Machine Learning Blog

SageMaker features and capabilities help developers and data scientists get started with natural language processing (NLP) on AWS with ease. The integration for this solution involves using Hugging Face’s pre-trained speaker diarization model using the PyAnnote library.

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What Do Data Scientists Do? A Guide to AI Maturity, Challenges, and Solutions

DataRobot Blog

Once an organization has identified its AI use cases , data scientists informally explore methodologies and solutions relevant to the business’s needs in the hunt for proofs of concept. These might include—but are not limited to—deep learning, image recognition and natural language processing. Download Now.

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Deploying a Vision Transformer Deep Learning Model with FastAPI in Python

PyImageSearch

To learn how to effectively deploy a Vision Transformer model with FastAPI and perform inference via exposed APIs, just keep reading. Jump Right To The Downloads Section What Is FastAPI? Originally designed for natural language processing, Transformers excel at capturing long-range dependencies within data.

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Enhance speech synthesis and video generation models with RLHF using audio and video segmentation in Amazon SageMaker

AWS Machine Learning Blog

Complete the following steps for manual deployment: Download these assets directly from the GitHub repository. Make sure you’re updating the data model ( updateTrackListData function) to handle your custom fields. The assets (JavaScript and CSS files) are available in our GitHub repository. Host them in your own S3 bucket.

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Accelerating Mixtral MoE fine-tuning on Amazon SageMaker with QLoRA

AWS Machine Learning Blog

Although QLoRA reduces computational requirements and memory footprint, FSDP, a data/model parallelism technique, will help shard the model across all eight GPUs (one ml.p4d.24xlarge 24xlarge ), enabling training the model even more efficiently. The results can be used for recommendation engines.