Remove BERT Remove Deep Learning Remove Explainability
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InstructAV: Transforming Authorship Verification with Enhanced Accuracy and Explainability Through Advanced Fine-Tuning Techniques

Marktechpost

With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. This lack of explainability is a gap in academic interest and a practical concern. Existing methods for AV have advanced significantly with the use of deep learning models.

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Is Traditional Machine Learning Still Relevant?

Unite.AI

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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The Black Box Problem in LLMs: Challenges and Emerging Solutions

Unite.AI

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. Flawed Decision Making The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors.

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Design Patterns in Python for AI and LLM Engineers: A Practical Guide

Unite.AI

I'll explain each pattern with practical AI use cases and Python code examples. Let’s explore some key design patterns that are particularly useful in AI and machine learning contexts, along with Python examples. BERT, GPT, or T5) based on the task. tabular vs. unstructured text).

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Recent developments in Generative AI for Audio

AssemblyAI

In this article, we take an overview of some exciting new advances in the space of Generative AI for audio that have all happened in the past few months , explaining where the key ideas come from and how they come together to bring audio generation to a new level. This blog post is part of a series on generative AI.

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Evolving Trends in Data Science: Insights from ODSC Conference Sessions from 2015 to 2024

ODSC - Open Data Science

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. The Deep Learning Boom (20182019) Between 2018 and 2019, deep learning dominated the conference landscape.

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Journey to AI blog

Figure 1: Framework for estimating Scope3 emissions using large language models We conducted extensive experiments involving several cutting-edge LLMs including roberta-base, bert-base-uncased, and distilroberta-base-climate-f. Additionally, we explored non-foundation classical models based on TF-IDF and Word2Vec vectorization approaches.

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