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…

“”“Process the WikiText dataset for training the BERT model. Using Hugging Face datasets library. ““”   import time import random from typing import Iterator   import tokenizers from datasets import load_dataset, Dataset   # path and name of each dataset…

In this article, you will learn how to use Docker to package, run, and ship a complete machine learning prediction service, covering the workflow from training a model to serving it as an API and distributing it as a container…