Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by… Read More »Using Learning Rate Schedule in PyTorch Training Adrian Tam MachineLearningMastery.com
In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Stable Diffusion is a deep learning model that allows you to generate realistic, high-quality images and stunning art in just a few seconds.… Read More »Fine-tune text-to-image Stable Diffusion models with Amazon SageMaker JumpStart Vivek Madan AWS Machine Learning Blog
Table of Contents Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning 🙌🏻 Introduction Configuring Your Development Environment Having Problems Configuring Your Development Environment? 🤔 What Is JAX? autograd XLA 👀 What Is JAX (revisited)? ⬇️… Read More »Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning Aritra Roy Gosthipaty and Ritwik Raha PyImageSearch
Dropout is a simple and powerful regularization technique for neural networks and deep learning models. In this post, you will discover the Dropout regularization technique and how to apply it to your models in PyTorch models. After reading this post, you will know: How… Read More »Using Dropout Regularization in PyTorch Models Adrian Tam MachineLearningMastery.com
RAID (redundant array of independent disks) is a way of combining multiple physical disk drives into one logical unit, which can increase performance, redundancy, and capacity depending on the RAID level used. In this blog post, we will discuss the different RAID levels, their… Read More »Understanding the Different Raid Levels: An Introduction Seb Programmathically
Posted by Parker Riley, Software Engineer, and Jan Botha, Research Scientist, Google Research Many languages spoken worldwide cover numerous regional varieties (sometimes called dialects), such as Brazilian and European Portuguese or Mainland and Taiwan Mandarin Chinese. Although such varieties are often mutually intelligible to their… Read More »FRMT: A benchmark for few-shot region-aware machine translation Google AI Google AI Blog
Modern model pre-training often calls for larger cluster deployment to reduce time and cost. At the server level, such training workloads demand faster compute and increased memory allocation. As models grow to hundreds of billions of parameters, they require a distributed training mechanism that… Read More »Scaling Large Language Model (LLM) training with Amazon EC2 Trn1 UltraClusters K.C. Tung AWS Machine Learning Blog
It’s been well publicized that Google’s Bard made some factual errors when it was demoed, and Google paid for these mistakes with a significant drop in their stock price. What didn’t receive as much news coverage (though in the last few days, it’s been… Read More »Sydney and the Bard Mike Loukides AI & ML – Radar
Posted by the TensorFlow Datasets team Datasets landscape has changed a lot since TensorFlow Datasets (TFDS) was introduced about 4 years ago: TFDS made sharing or re-using a dataset significantly easier, and transformed the datasets landscape by inspiring other ML tools, libraries and services.… Read More »TensorFlow Datasets is turning 4! noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog
Enterprises across the globe are looking to utilize multiple data sources to implement a unified search experience for their employees and end customers. Considering the large volume of data that needs to be examined and indexed, the retrieval speed, solution scalability, and search performance… Read More »New expanded data format support in Amazon Kendra Rishabh Yadav AWS Machine Learning Blog