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Building AI is easy. Deploying it reliably is the real challenge. Learn how to move from prototypes to production with PyTorch, transformers, RAG, evaluation frameworks, and scalable deployment strategies. #AI #MachineLearning #PyTorch #LLMOps
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How Army Uses Machine Learning!
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Is the weather outside a factor in the preparedness of troops for battle? Additionally, how does the cumulative stress from training, exercises, or overseas deployments impact them?
Before addressing these inquiries, the Army must find a way to gauge the influence of the environment on its personnel. One method to achieve this is by utilizing serious incident reports (SIRs), which record detrimental behaviors like suicide, threats, accidents, domestic violence, vandalism, and identity theft. Army uses machine learning and artificial intelligence for these analytics.
"Weโve got all this data. We're just trying toโฆmeasure the right things, figure out what's connected, and then use it to generate insights and make better decisions,โ emphasized Lt. Col. Jonathan Bate, commander for the Battalion, 23rd Infantry Regiment.
โResearching weather trends and prior harmful behaviors, unit type, operational tempo, we aim to determine the risk of a unit experiencing harmful behavior or a serious incident reportโฆso we're able to quantify the risk level."
The Army's shift in focus toward soldiers' quality of life and culture as a strategy to enhance readiness for war was officially announced by senior Army leaders before Congress in 2021.
Within the Infantry Division, a dedicated data team has been diligently working on data analytics projects for over a year, and their efforts are beginning to yield results.
One notable project is the unit risk forecasting tool, which utilizes a data regression model to analyze the correlation between patterns in serious incident reports and the likelihood of a unit experiencing another serious incident. This initiative aims to predict and mitigate harmful soldier behaviors, enabling command teams to proactively implement effective interventions.
The updated model was launched in February, and it now tracks SIRs, which are automatically forwarded up the chain of command. It also triggers email alerts when units accumulate an excessive number of SIRs, enabling leaders to identify potential issues more efficiently.
The 1st Stryker Brigade Combat Team, also known as the Raider Brigade, comprises approximately 4,500 soldiers organized into 37 companies, troops, and batteries.
When the model issues a SIR alert, leaders, including Bate who also receives the alerts, can promptly focus on the specific struggling company for targeted intervention.
The unit risk forecasting tool is just one of the methods the division is employing to prioritize soldier safety through data utilization.
The Infantry Division boasts 195 dashboards accessible to leaders throughout, facilitating a more comprehensive understanding of their respective organizations. Among these dashboards, approximately 100 enable users to delve deeper into company or battalion-specific trends for more detailed insights.
The majority of the dashboards extract data from the Armyโs Vantage platform, which has been supported by the AI company Palantir since 2019. Additionally, some dashboards rely on customized databases that aggregate previous data for analytics and machine learning.
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Ukrainian Mathematician Awarded Prestigious Fields Medal! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode
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The State of PyTorch 2026 https://t.co/LivbV5rlow via @Techstrongai & @sjvn
#PyTorch is at the heart of #AI development and, increasingly, the software layer through which hardware vendors compete for AI workloads.
Context Engineering! Multi-Agent Systems: Move Beyond Prompting to Build a Context Engine: A Transparent Architecture of Context and Reasoning! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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Google DeepMind ใฎ่ซๆใA Fix for Softmax and Cross-Entropyใใ Mac ใงๅ็พใใพใใใๆๅคฑใฎ่ทณใญไธใใใฏๅนณๅ21ๅโ0ๅใใใ fp32 ใงใฏๆ็ธพใฎๅทฎใฏๅบใใLoRA ใงใ่ทณใญใชใใ้ญๆณใงใฏใชใใๆกไปถใใใใฃใใจใใฎๅฎๅ
จ่ฃ
็ฝฎใงใใ
#็ๆAI #ๆฉๆขฐๅญฆ็ฟ #PyTorch https://t.co/FNufCC8DVu
El dropout apaga al azar cerca del 50% de las neuronas en cada paso. Ninguna puede contar con otra. Y generaliza mejor.
Lo que mรกs me gusta del paper: con 12 neuronas entrenas a la vez 4.096 redes.
Ojo en #PyTorch: el p de nn.Dropout es la probabilidad de apagar. #DeepLearning

Out of GPU memory during training? You may not need a smaller model.
Gradient checkpointing stores only some forward activations and recomputes the rest during backward.
The trade: far less activation memory for about one extra forward pass.
#DeepLearning #PyTorch
Top English Math Books to Read! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode
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Top #Books on #ComputerEngineering / #NLProc / AI. #BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
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@deepaksinghml @flappyairplanes Latch is such a cool project, Deepak! Generating training megakernels straight from PyTorch, and open-sourcing it, puts a big-lab idea in everyone's hands. ๐ #PyTorch #OpenSource
GAN 96% โ An Exceptionally High Performance Level - Brain Research! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #HealthTech #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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GPU Parallel! Accelerated Robotics in Milliseconds. - by Glen Chou - Georgia Institute of Technology! Atlanta,
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #HPC #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Robotics #Coding #100DaysofCode
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GPT & Generative AI and LLM! @DataSciConnect #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #Books #100DaysofCode
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