If you want to become a full-stack data scientist then start from data science and to become MLE you can go with ML as well
MACHINE LEARNING
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Team structure and ethical considerations in AI dataset creation
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I would say: smaller teams of full time people in a single org, simpler performance goals, more paid compute, no specific goals on ethical sourcing or social impact of the dataset creation process
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AI Evolution Reshapes Modern Advertising Industry Landscape
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How Artificial Intelligence has Evolved in the Advertising Industry https://
analyticsinsight.net/how-artificial
-intelligence-has-evolved-in-the-advertising-industry/
… #ArtificialIntelligence #AI #Advertising #MachineLearning #DataScience #innovation #IoT #DigitalMarketing #100DaysOfCode #technology #programming #DigitalTransformation -
Mastering Atari Games with Limited Data: Deep Learning
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Mastering Atari Games with Limited Data Ye et al.: https://
arxiv.org/abs/2111.00210 #MachineLearning #DeepLearning #ArtificialIntelligence -
What Will Artificial Intelligence Do To Us?
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What Will #ArtificialIntelligence Do To Us? https://
currentaffairs.org/2022/11/what-w
ill-artificial-intelligence-do-to-us
… @curaffairs #AI #DeepLearning #Robots #BigData #Analytics #MachineLearning #100DaysofCode #IoT #serverless #womenwhocode #Rstats #Python #DigitalTransformation #TensorFlow #DataScience -
Why Tech Calls Machine Learning Systems ‘AI’
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Machine-learning systems are problematic. That’s why tech bosses call them ‘AI’ https://
theguardian.com/commentisfree/
2022/nov/05/machine-learning-systems-are-problematic-thats-why-tech-bosses-call-them-ai
… @guardian #AI #MachineLearning #DataScience #Robots #BigData #Analytics #100DaysofCode #IoT #serverless #womenwhocode #Python #DigitalTransformation #TensorFlow #DeepLearning -
Parallel Experiments Challenge in Multilingual Machine Learning Project
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I also think there was a will to experiments on too many aspects at the same time and in parallel in BS: try a new organisation, a multilingual model with low-ressources languages, a new way of building a dataset (crowd sourcing of sources), attempts to train in fp16, etc etc
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K-Shot Learning for Non-RLHF Tuned LLMs
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I’ve never seen it work on anything other than RLHF-tuned GPT-3. I’ve wondered if you could k-shot a non-RLHF model with meta-examples of instruction templates followed by input/output pairs for each, but haven’t tried it.