3 Smart Ways to Encode Categorical Features for Machine Learning
In this article, you will learn three reliable techniques — ordinal encoding, one-hot encoding, and…
Category: AI
Evaluating Perplexity on Language Models
A language model is a probability distribution over sequences of tokens. When you train a…
Practical Agentic Coding with Google Jules
Practical Agentic Coding with Google JulesImage by Editor Introducing Google Jules If you have an…
Training a Model on Multiple GPUs with Data Parallelism
import dataclasses import os import datasets import tqdm import tokenizers import torch import torch.distributed…
Train a Model Faster with torch.compile and Gradient Accumulation
Training a language model with a deep transformer architecture is time-consuming. However, there are techniques…
Training a Model with Limited Memory using Mixed Precision and Gradient Checkpointing
Training a language model is memory-intensive, not only because the model itself is large but…
Rotary Position Embeddings for Long Context Length
Rotary Position Embeddings (RoPE) is a technique for encoding token positions in a sequence. It…
The Machine Learning Engineer’s Checklist: Best Practices for Reliable Models
The Machine Learning Engineer’s Checklist: Best Practices for Reliable ModelsImage by Editor Introduction Building newly…
Top 5 Vector Databases for High-Performance LLM Applications
Top 5 Vector Databases for High-Performance LLM ApplicationsImage by Editor Introduction Building AI applications often…
The Real Cost of Inaction: How Silos Hurt Productivity for Data Scientists (Sponsored)
The Real Cost of Inaction: How Silos Hurt Productivity for Data Scientists (Sponsored) Source link