At a low level, there is no magic in machine learning — just lots of straightforward mathematics and systems engineering. And somehow the end result is entirely magical.
MACHINE LEARNING
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Diving into Machine Learning Algorithms and AI Innovation
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Diving into the world of Machine Learning Algorithms! Understand the core methods driving AI innovation and data insights. #MachineLearning #Algorithms #TechTalk via @ingliguori
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Announcing the Launch of OverflowAI for Artificial Intelligence
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Announcing OverflowAI
https://bit.ly/3OPCbe8 #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Free Stanford Course on Natural Language Processing with Deep Learning
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100% free course from Standford: "Natural Language Processing with Deep Learning." https://
youtube.com/playlist?list=
PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ
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Topic Modeling for Legal Document Classification and Analysis
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9. Legal and Document Analysis: Legal Document Classification: Topic modeling can categorize legal documents, contracts, and case law to assist legal professionals in organizing and retrieving relevant information.
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Topic Modeling for Electronic Health Records Analysis and Medical Research
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8. Healthcare and Medical Records: Patient Records Analysis: Topic modeling can analyze electronic health records to identify patterns, trends, and associations in patient data, assisting in medical research and decision-making.
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Topic Modeling for News Clustering and Media Analysis
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7. News and Media Analysis: News Clustering: Topic modeling can group news articles into clusters based on their content, enabling users to explore news from different perspectives and sources.
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Topic Modeling for Academic Research Literature Analysis
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6. Academic Research: Literature Analysis: Researchers can use topic modeling to analyze large volumes of research papers and identify the main themes, trends, and connections in a particular field over time.
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Offline Reinforcement Learning with Implicit Q-Learning
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Offline Reinforcement Learning with Implicit Q-Learning Kostrikov et al.: https://
arxiv.org/abs/2110.06169 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
Data Augmentation Techniques for Texture-based Model Training
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Since it's a domain-specific dataset, and in particular textures, you can do many augmentations to get more out of it… Training at 256×256, that's approximately 27,6k unique patches and with flip/rot augmentations about 221k! (Training random patches works even better.)