To Data & Beyond Week 21 Summary
Every week, To Data & Beyond delivers daily newsletters on data science and AI, focusing on practical topics. This newsletter summarizes the featured article in the 21st week of 2024. You can find them here if you're interested in reading the complete letters. Don't miss out—subscribe here to receive them directly in your email.
Table of Contents:
1. Top Important Computer Vision Papers for the Week from 13/05 to 19/05
Every week, researchers from top research labs, companies, and universities publish exciting breakthroughs in various topics such as diffusion models, vision language models, image editing and generation, video processing and generation, and image recognition.
This article provides a comprehensive overview of the most significant papers published in the Third Week of May 2024, highlighting the latest research and advancements in computer vision.
Whether you’re a researcher, practitioner, or enthusiast, this article will provide valuable insights into the state-of-the-art techniques and tools in computer vision.
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2. Top Important LLMs Papers for the Week from 13/05 to 19/05
Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the latest progress.
This article summarizes some of the most important LLM papers published during the Third Week of May 2024. The papers cover various topics shaping the next generation of language models, from model optimization and scaling to reasoning, benchmarking, and enhancing performance.
Keeping up with novel LLM research across these domains will help guide continued progress toward models that are more capable, robust, and aligned with human values.
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3. Introducing Kudra: No Code Data Extraction From Any Document Tool
KUDRA is a powerful intelligent document processing platform that offers comprehensive AI services to automate data extraction from any document. It leverages the latest AI technologies to extract entities, relationships, and tables, and to create summaries from your documents.
In this article, we will explore what Kudra is and provide a step-by-step tutorial with Dr. Walid Amamou, the founder of Kudra, on how to build your own custom pipelines for various document processing tasks and integrate large language models (LLMs) within your pipeline.
We will also discuss how to host your own AI models from HuggingFace and integrate them into your pipeline. Finally, we will show you how to create an API for this pipeline to integrate it into your application.
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4. How to Stay Updated with LLM Research & Industry News?
In the rapidly evolving landscape of Large Language Models (LLMs), staying abreast of the latest research breakthroughs and industry developments is paramount for professionals and enthusiasts alike. This blog serves as a comprehensive guide, presenting a curated list of resources tailored to cater to this need.
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The blog begins by spotlighting the pioneers and thought leaders in LLM research, providing insights into their work and contributions to the field. It then transitions to examining key players driving innovation and application within the LLM industry. Additionally, it explores prominent organizations dedicated to advancing LLM research and fostering collaboration within the community.
Furthermore, the blog identifies influential individuals and content creators shaping discourse and disseminating valuable insights across various platforms. It delves into the realm of newsletters and blogs, highlighting essential sources for staying updated on the latest trends and developments in LLM research and industry.
Whether one is an aspiring researcher, industry practitioner, or simply intrigued by the capabilities of LLMs, this blog equips readers with the essential resources to remain informed and engaged in this dynamic domain.
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5. Building & Deploying a Speech Recognition System Using the Whisper Model & Gradio
Speech recognition is the task of converting spoken language into text. This article provides a comprehensive guide on building and deploying a speech recognition system using OpenAI’s Whisper model and Gradio.
The process begins with setting up the working environment, including the installation of necessary packages such as HuggingFace’s transformers and datasets, as well as soundfile, librosa, and radio.
The dataset used is the LibriSpeech corpus, loaded from the HuggingFace dataset hub. Detailed instructions are provided for exploring and listening to the dataset samples.
Next, the article explains how to construct a Transformers pipeline utilizing the distilled version of the Whisper model, optimized for faster and smaller speech recognition tasks while maintaining high accuracy. The deployment section demonstrates how to create a user-friendly web application using Gradio.
This application allows for real-time speech transcription via microphone input or uploaded audio files. The final product is a robust, interactive interface for speech-to-text conversion, complete with step-by-step code examples and deployment instructions.
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6. Hands-On Introduction to Open AI Function Calling
A few months ago, OpenAI introduced a new capability to its API, enhancing its most recent models to accept additional parameters for function calling. These models are now fine-tuned to determine when it's relevant to call one of these functions. In this article, we'll explore how to use this feature effectively, along with tips and tricks for optimal results.
We'll use the OpenAI SDK to demonstrate this new capability, imagining a function we find valuable to provide to the language model. We'll delve into what makes a function "interesting" and discuss various use cases for this new parameter.
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7. Testing Prompt Engineering-Based LLM Applications
Once such a system is built, how can you assess its performance? As you deploy it and users interact with it, how can you monitor its effectiveness, identify shortcomings, and continually enhance the quality of its responses?
In this article, we will explore and share best practices for evaluating LLM outputs and provide insights into the experience of building these systems. One key distinction between this approach and traditional supervised machine learning applications is the speed at which you can develop LLM-based applications.
As a result, evaluation methods typically do not begin with a predefined test set; instead, you gradually build a set of test examples as you refine the system.
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