BERT is an early transformer-based model for NLP tasks that’s small and fast enough to train on a home computer. Like all deep learning models, it requires a tokenizer to convert text into integer tokens. This article shows how to…

In this article, you will learn why decision trees sometimes fail in practice and how to correct the most common issues with simple, effective techniques. Topics we will cover include: How to spot and reduce overfitting in decision trees. How…

This article shows how Shannon’s information theory connects to the tools you’ll find in modern machine learning. We’ll address entropy and information gain, then move to cross-entropy, KL divergence, and the methods used in today’s generative learning systems. Here’s what’s…

import dataclasses   import datasets import torch import torch.nn as nn import tqdm     @dataclasses.dataclass class BertConfig:     “”“Configuration for BERT model.”“”     vocab_size: int = 30522     num_layers: int = 12     hidden_size: int = 768     num_heads: int = 12     dropout_prob: float…

In this article, you will learn how a transformer converts input tokens into context-aware representations and, ultimately, next-token probabilities. Topics we will cover include: How tokenization, embeddings, and positional information prepare inputs What multi-headed attention and feed-forward networks contribute inside…

BERT is a transformer-based model for NLP tasks that was released by Google in 2018. It is found to be useful for a wide range of NLP tasks. In this article, we will overview the architecture of BERT and how…