Remove Categorization Remove Deep Learning Remove Explainability
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AI for Universal Audio Understanding: Qwen-Audio Explained

AssemblyAI

This approach eliminates the scalability constraints of prior models, such as the need for manual task categorization or reliance on dataset identifiers during training, aimed at preventing a one-to-many interference problem , typical of multi-task training scenarios.

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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Journey to AI blog

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

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Building Reliable Machine Learning Models: Lessons from Brian Lucena

ODSC - Open Data Science

Why Gradient Boosting Continues to Dominate Tabular DataProblems Machine learning has seen the rise of deep learning models, particularly for unstructured data such as images and text. CatBoost : Specialized in handling categorical variables efficiently. This is where uncertainty estimation becomescrucial.

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This AI Paper from King’s College London Introduces a Theoretical Analysis of Neural Network Architectures Through Topos Theory

Marktechpost

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Deep Learning Challenges in Software Development

Heartbeat

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. It is a tremendous tool with the ability to completely alter numerous sectors.

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Machine Learning vs. Deep Learning - A Comparison

Heartbeat

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

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Policy Gradient Algorithm’s Mathematics Explained with PyTorch Implementation

Towards AI

RL algorithms can be generally categorized into two groups i.e., value-based and policy-based methods. Policy Gradient Method As explained above, Policy Gradient (PG) methods are algorithms that aim to learn the optimal policy function directly in a Markov Decision Processes setting (S, A, P, R, γ).