Traditional tokenization techniques face limitations with vocabularies, particularly with respect to unknown words, out-of-vocabulary (OOV) tokens, and the sparsity of […]
Byte Pair Encoding (BPE) Explained: How It Fuels Powerful LLMs Read More »
LLMs handle out-of-vocabulary (OOV) words or tokens by leveraging their tokenization process, which ensures that even unfamiliar or rare inputs
How do LLMs Handle Out-of-vocabulary (OOV) Words? Read More »
Evaluating the effectiveness of a prompt is crucial to harnessing the full potential of Large Language Models (LLMs). An effective
Quantifying Prompt Quality: Evaluating The Effectiveness Of A Prompt Read More »
Ensemble Learning aims to improve the predictive performance of models by combining multiple learners. By leveraging the collective intelligence of
Ensemble Learning: Leveraging Multiple Models For Superior Performance Read More »
The application of machine learning (ML) in sectors such as healthcare, finance, and social media poses risks, as these domains
Protecting Privacy in the Age of AI Read More »
Autoencoder is a type of neural network architecture designed for unsupervised learning which excel in dimensionality reduction, feature learning, and
Autoencoders in NLP and ML: A Comprehensive Overview Read More »
Federated Learning (FL) decentralizes the conventional training of ML models by enabling multiple clients to collaboratively learn a shared model
Decentralized Intelligence: A Look at Federated Learning Read More »
Imbalanced dataset is one of the prominent challenges in machine learning. It refers to a situation where the classes in
Imbalanced Data: A Practical Guide Read More »
Layer normalization has emerged as a pivotal technique in the optimization of deep learning models, particularly when it comes to
Deep Learning Optimization: The Role of Layer Normalization Read More »
This article summarizes the content of the source, “The Efficiency Spectrum of Large Language Models: An Algorithmic Survey,” focusing on
Pushing the Boundaries of LLM Efficiency: Algorithmic Advancements Read More »
With the advances of deep learning come challenges, most notably the issue of overfitting. Overfitting occurs when a model learns
Regularization Techniques in Neural Networks Read More »
Vision Transformers (ViT) have emerged as a groundbreaking architecture that has revolutionized how computers perceive and understand visual data. Introduced
Dissecting the Vision Transformer (ViT): Architecture and Key Concepts Read More »
