Flipboard

MARCH 27, 2023

Table of Contents What’s New in PyTorch 2.0? torch.compile Configuring Your Development Environment Installation Verification Overview of PyTorch 2.0 What’s New in PyTorch 2.0? torch.compile torch.compile Definition Accelerating DNNs with PyTorch 2.0 The stable release of PyTorch 2.0 just keep reading.

Deep Learning 117

Deep Learning Deep Learning Python Natural Language Processing 117

The MLOps Blog

NOVEMBER 14, 2023

This is where visualizations in ML come in. Graphical representations of structures and data flow within a deep learning model make its complexity easier to comprehend and enable insight into its decision-making process. In this article, we’ll explore a wide range of deep learning visualizations and discuss their applicability.

Deep Learning 111

Deep Learning Deep Learning Data Scientist Machine Learning 111

AWS Machine Learning Blog

APRIL 8, 2024

This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion and token level log probabilities).

AWS 84

AWS Deep Learning Deep Learning Machine Learning 84

AWS Machine Learning Blog

MARCH 2, 2023

Sentiment analysis and other natural language programming (NLP) tasks often start out with pre-trained NLP models and implement fine-tuning of the hyperparameters to adjust the model to changes in the environment. The architectural complexity increases when a single model training run requires multiple GPUs.

Clustering 76

Clustering AWS Deep Learning Deep Learning 76

AWS Machine Learning Blog

MARCH 6, 2023

Storage – We see data loading and checkpointing done in two ways, depending on skills and preferences: with an Amazon FSx Lustre file system, or Amazon Simple Storage Service (Amazon S3) only. We recommend using a cloud-optimized library, such as SageMaker sharded data parallelism, but self-managed and open-source libraries can also work.

AWS 78

AWS Clustering ML ML 78

The MLOps Blog

AUGUST 11, 2023

Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. However, this efficient system does not just operate independently – it necessitates a comprehensive architectural approach and thoughtful design consideration.

ML 52

ML ML Machine Learning Machine Learning 52

Mlearning.ai

JUNE 8, 2023

Meet AI's multitool: Vector embeddings | Google Cloud Blog Embedding applications Recommendation systems (i.e. Hidden secret to empower semantic search This is the third article of building LLM-powered AI applications series. To enable semantic search, we need something called embedding/vector/vector embedding.

Machine Learning 52

Machine Learning Machine Learning Clustering Database 52