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Transformer models: A guide to understanding different transformer architectures and their uses

Data Science Dojo

Natural language processing (NLP) and large language models (LLMs) have been revolutionized with the introduction of transformer models. While we have talked about the details of a typical transformer architecture, in this blog we will explore the different types of the models. How to categorize transformer models?

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6 data science projects to boost your data science portfolio

Data Science Dojo

In this blog, we will discuss the latest 6 projects that can escalate your data science career and boost your data science portfolio in a competitive era. With modern analytics tools becoming easier than ever, one needs to do something more than being able to train a model or plot a graph to stand out from the crowd.

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AI Emotion Recognition Using Computer Vision

Heartbeat

Visual AI Emotion Recognition Emotion recognition is one such derivative of computer vision that involves analysis, interpretation, and classification of the emotional quotient using multimodal features like visual data, body language and gestures to assess the emotional state of the person.

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Harnessing LLM chatbots: Real-life applications, building techniques and LangChain’s Finetuning

Data Science Dojo

The next generation of Language Model Systems (LLMs) and LLM chatbots are expected to offer improved accuracy, expanded language support, enhanced computational efficiency, and seamless integration with emerging technologies. These advancements indicate a higher level of versatility and practicality compared to the previous models.

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning Blog

In recent years, large language models (LLMs) have gained attention for their effectiveness, leading various industries to adapt general LLMs to their data for improved results, making efficient training and hardware availability crucial. This is a guest post by Mark McQuade, Malikeh Ehghaghi, and Shamane Siri from Arcee.

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Conversational AI use cases for enterprises

IBM Journey to AI blog

In addition, ML techniques power tasks like speech recognition, text classification, sentiment analysis and entity recognition. DL models can improve over time through further training and exposure to more data. DL models can improve over time through further training and exposure to more data.

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Generative AI use cases for the enterprise

IBM Journey to AI blog

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

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