Agents: An Open-source Framework for Autonomous Language Agents Zhou et al.: https://
arxiv.org/abs/2309.07870 #ArtificialIntelligence #DeepLearning #MachineLearning
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Open-source Framework for Autonomous Language Agents
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Learning Agents Getting Started Course Guide
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Learning Agents – Getting Started | Course https://
bit.ly/3EAtXQR
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Handling Large Documents Exceeding Context Windows
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ah. curious to see approaches to large documents that wont fit in the context window 🙂
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How Does AI Understand Document Context for Code Generation?
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you said you were going to get it to write code to modify the document. when you give it instructions "change last ? to ." the function call is "replace_text("…?", "….")" (or something. it knows what the last sentence is…. how?
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JavaScript Retrieval Functionality in LangChain Framework
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Great to have this retrieval functionality in JS See the underlying prompt here: https://
smith.langchain.com/hub/jacob/mult
i-query-retriever
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Interactive Virtual Panel on LLMs: Open-source vs Commercial Solutions
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Don't miss our interactive virtual panel with #LLM experts from @llama_index
, Bank of America & @predibase Topics: Open-source vs. commercial LLMs High-value use cases Tips for customization (#RAG, #finetuning) Overcoming common pitfalls https://
pbase.ai/3PDq4Qz -
Benefits of Pair Programming in AI Research and Development
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Pair programming isn’t standard at most companies and basically non-existent in academia, but I’ve been doing it with @hwchung27 for almost a year now. While it naively seems slower to code individually, I’ve realized that there are many benefits: (1) In AI, what you work on can
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Training YOLOv8 for Real-Time Pothole Detection: Model Comparison
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🚀YOLOv8 is one of the fastest and most accurate YOLO models out there.https://t.co/l9JUDdcJkD
— Satya Mallick (@LearnOpenCV) 14 septembre 2023
Why not leverage it for training a real-world dataset? Let's train YOLOv8 to detect potholes in real time and compare performance between three different YOLOv8 models.… pic.twitter.com/IocN7SXl5eYOLOv8 is one of the fastest and most accurate YOLO models out there. https://
learnopencv.com/train-yolov8-o
n-custom-dataset/
… Why not leverage it for training a real-world dataset? Let's train YOLOv8 to detect potholes in real time and compare performance between three different YOLOv8 models. -
Frame Generation Optimization: From DeepDream to Neural Art
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Each frame took 15 minutes! The original technique was ~1000 optimization steps, similar to Deepdream. You could start from the previous frame and do fewer steps, but it didn't look great. https://
arxiv.org/abs/1508.06576