Tag: AI/ML Techniques

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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.

This post is divided into five parts; they are: • Why Normalization is Needed in Transformers • LayerNorm and Its Implementation • Adaptive LayerNorm • RMS Norm and Its Implementation • Using PyTorch’s Built-in Normalization Normalization layers improve model quality in deep learning.

Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.

This post is divided into four parts; they are: • Why Attention Masking is Needed • Implementation of Attention Masks • Mask Creation • Using PyTorch’s Built-in Attention In the