10 NumPy One-Liners to Simplify Feature Engineering
When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
In a
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.