It would be difficult to argue that word embeddings — dense vector representations of words — have not dramatically revolutionized the field of natural language processing (NLP) by quantitatively capturing semantic relationships between words.
Versatile, interpretable, and effective for a variety of use cases, decision trees have been among the most well-established machine learning techniques for decades, widely used for classification and regression tasks.
When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
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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 Skip Connections in Transformer Models • Pre-norm vs Post-norm Transformer Architectures Transformer models, like other deep learning models, stack many layers on top of each other.
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 Works • Implementation of MoE in Transformer Models The Mixture of Experts (MoE) concept was first introduced in 1991 by
Interested in leveraging a large language model (LLM) API locally on your machine using Python and not-too-overwhelming tools frameworks? In this step-by-step article, you will set up a local API where you’ll be able to send prompts to an LLM downloaded on your machine and obtain responses back.
This post is divided into three parts; they are: • Why Linear Layers and Activations are Needed in Transformers • Typical Design of the Feed-Forward Network • Variations of the Activation Functions The attention layer is the core function of a transformer model.