My introduction to Deep Learning
I had to work a lot to get an answer to each question, to have a basic understanding of Deep Learning. As I tackled through, and got all the math and idea to simple-enough level of complexity to understand something essential or basic, I reached the need to write this text: https://github.com/tambetvali/LaegnaAIBasics It's quite a complete intro to DL basics, and it's also having essential mathematical simplicity to get your own ideas rather than looking at cryptic code, I guess: I will add there as I get more things simple, over time (first absorbing the ideas long enough to be able to write with some clarity).
Using Neural Network to Learn Profitable Trading in the FOREX Markets
I am using Neural Networks (NN) to teach them how to recognize profitable trading opportunities in the Foreign Exchange (FOREX) markets, using 10 currencies simultaneously. I am using 3rd-order Cubic Splines as input to give the NNs a sense of how the critical variables change over time. I am using free FOREX historical trading data to train the NNs how to trade profitably in the future. I don't just feed the trading levels of the FOREX currency pairs as input to the NNs. Instead, I use a variation of the computed DXY Index for all 10 currencies in order to isolate the value change of each of the individual currencies, using Cubic Splines to detail how those values change over various time periods. The end result is Neural Networks that recognize which currencies to Buy and which ones to Sell at the most profitable times. If anyone is interested in the details, please reach out and I will provide more details.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.
Training and Inference of LLMs with PyTorch Fully Sharded Data Parallel and Better Transformer
In this blog we show how to perform efficient and optimized distributed training and inference of large language models using PyTorch’s Fully Sharded Data Parallel and Better Transformer implementations, on the Spark platform. In this implementation, we combine Microsoft Fabric for data preparation and model inference, and Azure Databricks for model training, having all our data under Microsoft Fabric’s OneLake. The code for this blog is available at this GitHub repository, as a series of PySpark notebooks for Microsoft Fabric and Azure Databricks.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.
