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Large language models (LLMs) are based on the transformer architecture, a complex deep neural network whose input is a sequence of token embeddings.

This article is divided into three parts; they are: • Creating a BERT Model the Easy Way • Creating a BERT Model from Scratch with PyTorch • Pre-training the BERT Model If your goal is to create a BERT model so that you can train it on your own data, using the Hugging Face `transformers` […]

Clustering models in machine learning must be assessed by how well they separate data into meaningful groups with distinctive characteristics.

Machine learning models often behave differently across environments.

This article is divided into four parts; they are: • Preparing Documents • Creating Sentence Pairs from Document • Masking Tokens • Saving the Training Data for Reuse Unlike decoder-only models, BERT’s pretraining is more complex.

This article is divided into two parts; they are: • Architecture and Training of BERT • Variations of BERT BERT is an encoder-only model.

In 1948, Claude Shannon published a paper that changed how we think about information forever.

You’ve learned about

LLMs

As a machine learning engineer, you probably enjoy working on interesting tasks like experimenting with model architectures, fine-tuning hyperparameters, and analyzing results.

A good language model should learn correct language usage, free of biases and errors.

Building machine learning models in high-stakes contexts like finance, healthcare, and critical infrastructure often demands robustness, explainability, and other domain-specific constraints.

When large language models first came out, most of us were just thinking about what they could do, what problems they could solve, and how far they might go.

  When we ask ourselves the question, ” what is inside machine learning systems? “, many of us picture frameworks and models that make predictions or perform tasks.

Understanding machine learning models is a vital aspect of building trustworthy AI systems.

Large language models (LLMs) exhibit outstanding abilities to reason over, summarize, and creatively generate text.

machine learning continues to evolve faster than most can keep up with.

Large language models (LLMs) are not only good at understanding and generating text; they can also turn raw text into numerical representations called embeddings.

Language models can generate text and reason impressively, yet they remain isolated by default.