Responsible Synthetic Data Creation for Fine-Tuning with RAFT Distillation
This blog will explore the process of crafting responsible synthetic data, evaluating it, and using it for fine-tuning models. We’ll also dive into Azure AI’s RAFT distillation recipe, a novel approach to generating synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.Interview with Jeremy Howard Fast.ai AI Application without a PhD
Fast.ai has made it their mission to make deep learning as accessible as possible, and in this interview fast.ai co-founder Jeremy Howard explains how to use their free software and courses to become an effective deep learning practitioner.Some recommendations for budding machine learning engineers:
First published on MSDN on Sep 19, 2018 Some Top Tips by Microsoft Software Engineer @DynamicWebPaige for getting started with DeepLearning1) Make sure your sample dataset is representative of your entire population - and remember that more data is usually - but not necessarily! - better.Smart Acc.: Web application based on Faster-RCNN architecture making it easier to find accommodations in London.
First published on MSDN on Jun 05, 2018 Guest post by Imperial College London, Microsoft Industry Exchange Project Group using Faster RCNN The project was developed by Nattapat Chaimanowong, Suampa Ketpreechasawat, Tomasz Bartkowiak, Lin Li, Yini Fang and Danlin Peng, a group of MSc.Azure Deep Learning and ND, NC series with NVidia
First published on MSDN on Dec 21, 2017 ND and NCv2 Series VMs generally availableND-Series virtual machines (VMs) are specifically designed for the needs of artificial intelligence (AI) and Deep Learning workloads, offering excellent performance for training and inference: ✓They are powered by NVIDIA Tesla P40 GPUs based on the new Pascal Architecture and provide excellent performance for FP32 (single precision floating point operations), for AI workloads utilizing CNTK, TensorFlow, Caffe, and other frameworks.
