🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary]

Core Insights from Stanford CS336 Lecture 15

🚀 Core Insights from Stanford CS336 Lecture 15: Large Language Model Alignment and Post-Training Processes Based on the content of the fifteenth lecture of the Stanford University CS336 course in Spring 2025, this article comprehensively and objectively reviews the key technical pipelines involved in the t... All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] IO page: https://learnbydoingwithsteven.github.io/ [https://learnbydoingwithsteven.github.io/] #learnbydoingwithsteven #AI #DeepLearning #Research #TechSummary #MachineLearning #LLM #ScalingLaws #NeuralNetworks #Innovation

🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary]

🚀 Stanford University CS336 Lecture 14 Language Model Data Filtering and Deduplication Algorithms [notebooklm summary] This lecture explores the data processing mechanics used for training language models, focusing specifically on quality filtering and data deduplication algorithms. Training data for language models i... All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #DeepLearning #Research #TechSummary #MachineLearning #LLM #ScalingLaws #NeuralNetworks #Innovation

The Agentic Architecture: Five Essential AI Terms Explained

✅ Recently, the evolution of Artificial Intelligence from conversational models to autonomous agents is driven by an instruction layer wrapped around Large Language Models (LLMs). ✅ The internal behavioral framework of an agent is defined by project-specific rules in the agents. ✅ While project rules are governed by agents. ✅ Connectivity and interoperability are crucial for autonomous agents to interact with external environments. All my links: ⁠https://linktr.ee/learnbydoingwithsteven⁠ [https://linktr.ee/learnbydoingwithsteven] Website: ⁠https://learnbydoingwithsteven.github.io⁠ [https://learnbydoingwithsteven.github.io/] #AIAgents #AgenticAI #SoftwareEngineering #LLMs #ModelContextProtocol #SystemSecurity #Microservices #AIAgentsOrchestration #learnbydoingwithsteven

The Agentic Architecture: Five Essential AI Terms Explained

✅ Recently, the evolution of Artificial Intelligence from conversational models to autonomous agents is driven by an instruction layer wrapped around Large Language Models (LLMs). ✅ The internal behavioral framework of an agent is defined by project-specific rules in the agents. ✅ While project rules are governed by agents. ✅ Connectivity and interoperability are crucial for autonomous agents to interact with external environments. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] Website: https://learnbydoingwithsteven.github.io [https://learnbydoingwithsteven.github.io/] #AIAgents #AgenticAI #SoftwareEngineering #LLMs #ModelContextProtocol #SystemSecurity #Microservices #AIAgentsOrchestration #learnbydoingwithsteven

Data Science Periodic Table Explained: A Strategic Map for Analytical Maturity and Workflow

✅ Recently, the landscape of data science is often perceived as a confusing collection of disparate terms and techniques, ranging from ETL to cross-validation. ✅ The horizontal structure of the table tracks the data data maturity lifecycle, moving from unrefined data to actionable insights. ✅ The columns of the table represent analytical activities that define the functional stages of the lifecycle, ranging from data acquisition to evaluation. ✅ The modeling and relationship estimation phase forms the core of pattern discovery, utilizing diverse statistical techniques. All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #DataScience #MachineLearning #ETL #DataGovernance #QuantumComputing #AI #ModelEvaluation #BigData #Analytics #learnbydoingwithsteven