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Amazon SageMaker built-in LightGBM now offers distributed training using Dask

AWS Machine Learning Blog

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning.

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A Practical Guide for identifying important features using Python

Mlearning.ai

Identifying important features using Python Introduction Features are the foundation on which every machine-learning model is built. What we are looking for in these algorithms is to output a list of features along with corresponding importance values. We will cover very rudimentary methods, along with quite sophisticated algorithms.

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How Open Source Developers Can Push the Universe’s Frontier

ODSC - Open Data Science

Be sure to check out his talk, “ Space Science with Python — Enabling Citizen Scientists ,” there! 2009, a paper by Postberg et al. Thankfully, as enthusiastic coders, we have THE major astronomy and space science tool to work on all these data, theories, and insights: Python ! Editor’s note: Dr.-Ing. was published in Nature.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

For example, to use the RedPajama dataset, use the following command: wget [link] python nemo/scripts/nlp_language_modeling/preprocess_data_for_megatron.py Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms.

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The Story Continues: Announcing Version 14 of Wolfram Language and Mathematica

Hacker News

Sometimes it’s a story of creating a superalgorithm that encapsulates decades of algorithmic development. Wolfram|Alpha has been able to deal with units ever since it was first launched in 2009 —now more than 10,000 of them. In addition, a new algorithm in Version 14.0 had 554 built-in functions; in Version 14.0 there are 6602.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

Solution overview In the following sections, we provide a step-by-step demonstration for fine-tuning an LLM for text generation tasks via both the JumpStart Studio UI and Python SDK. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

Solution overview In the following sections, we provide a step-by-step demonstration for fine-tuning an LLM for text generation tasks via both the JumpStart Studio UI and Python SDK. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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