Latest News from MachineLearningMastery
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Word Embeddings for Tabular Data Feature Engineering
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.
Decision Trees Aren’t Just for Tabular Data
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.
10 NumPy One-Liners to Simplify Feature Engineering
When building machine learning models, most developers focus on model architectures and hyperparameter tuning.
Securing FastAPI Endpoints for MLOps: An Authentication Guide
In today's AI world, data scientists are not just focused on training and optimizing machine learning models.
Skip Connections in Transformer 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.
5 Advanced RAG Architectures Beyond Traditional Methods
Retrieval-augmented generation (RAG) has shaken up the world of language models by combining the best of two worlds:
Mixture of Experts Architecture in Transformer Models
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
Your First Local LLM API Project in Python Step-By-Step
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.
Linear Layers and Activation Functions in Transformer Models
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.