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Have you ever tried connecting a language model to your own data or tools? If so, you know it often means writing custom integrations, managing API schemas, and wrestling with authentication.

For years, GitHub Copilot has served as a powerful pair programming tool for programmers, suggesting the next line of code.

Machine learning models built with frameworks like scikit-learn can accommodate unstructured data like text, as long as this raw text is converted into a numerical representation that is understandable by algorithms, models, and machines in a broader sense.

Powerful AI now runs on consumer hardware.

For data scientists, working with high-dimensional data is part of daily life.

Most forecasting work involves building custom models for each dataset — fit an ARIMA here, tune an LSTM there, wrestle with

  In languages like C, you manually allocate and free memory.

  If you’ve trained a machine learning model, a common question comes up: “How do we actually use it?” This is where many machine learning practitioners get stuck.

I have been building a payment platform using vibe coding, and I do not have a frontend background.

Suppose you’ve built your machine learning model, run the experiments, and stared at the results wondering what went wrong.

This article is divided into two parts; they are: • Data Parallelism • Distributed Data Parallelism If you have multiple GPUs, you can combine them to operate as a single GPU with greater memory capacity.

This article is divided into two parts; they are: • Using `torch.

This article is divided into three parts; they are: • Floating-point Numbers • Automatic Mixed Precision Training • Gradient Checkpointing Let’s get started! The default data type in PyTorch is the IEEE 754 32-bit floating-point format, also known as single precision.

If you have an interest in agentic coding, there’s a pretty good chance you’ve heard of

This article is divided into two parts; they are: • What Is Perplexity and How to Compute It • Evaluate the Perplexity of a Language Model with HellaSwag Dataset Perplexity is a measure of how well a language model predicts a sample of text.

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.