Category: AI Category
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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprises increasingly rely on large language models (LLMs) to deliver advanced services, but struggle to handle the computational costs of running models. A new framework, chain-of-experts (CoE), aims to make LLMs more resource-efficient while increasing their…
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The way we interact with our computers and smart devices is very different from previous years. Over the decades, human-computer interfaces have transformed, progressing from simple cardboard punch cards to keyboards and mice, and now extended reality-based AI agents that can converse with us in the same way as we do with friends. With each…
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Having worked with AI since 2018, I’m watching its slow but steady pick-up alongside the unstructured bandwagon-jumping with considerable interest. Now that the initial fear has subsided somewhat about a robotic takeover, discussion about the ethics that will surround the integration of AI into everyday business structures has taken its place. A whole new range…
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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Once upon a time, software ate the world. Now, AI is here to digest what’s left. The old model of computing, where apps ruled, marketplaces controlled access and platforms took their cut, is unraveling. What’s emerging…
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This tutorial is in four parts; they are: • The Core Text Generation Implementation • Contrastive Search: What are the Parameters in Text Generation? • Batch Processing and Padding • Tips for Better Generation Results Let’s start with a basic implementation that demonstrates the fundamental concept. Source link
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Data preparation is a step within the data project lifecycle where we prepare the raw data for subsequent processes, such as data analysis and machine learning modeling. Source link
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FastAPI is a modern and high-performance compliant web framework for building APIs with Python. Source link
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Overfitting is one of the most (if not the most!) common problems encountered when building machine learning (ML) models. Source link
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This tutorial is in two parts; they are: • Using DistilBart for Summarization • Improving the Summarization Process Let’s start with a fundamental implementation that demonstrates the key concepts of text summarization with DistilBart: import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM class TextSummarizer: def __init__(self, model_name=”sshleifer/distilbart-cnn-12-6″): “””Initialize the summarizer with a pre-trained model. Source link
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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The review app Yelp has provided helpful information to diners and other consumers for decades. It had experimented with machine learning since its early years. During the recent explosion in AI technology, it was still encountering…