When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
In today's AI world, data scientists are not just focused on training and optimizing machine learning models.
This post is divided into three parts; they are: • Why Skip Connections are Needed in Transformers • Implementation of…
Retrieval-augmented generation (RAG) has shaken up the world of language models by combining the best of two worlds:
This post covers three main areas: • Why Mixture of Experts is Needed in Transformers • How Mixture of Experts…
Interested in leveraging a large language model (LLM) API locally on your machine using Python and not-too-overwhelming tools frameworks? In…
This post is divided into three parts; they are: • Why Linear Layers and Activations are Needed in Transformers •…
This post is divided into five parts; they are: • Why Normalization is Needed in Transformers • LayerNorm and Its…
Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.