SkyAGI: Emerging human-behavior simulation capability in LLM This Python package implements the idea of Generative Agents (see image). Characters interact with each other. Each of them has a personality, a current status, and memories. https://
github.com/litanlitudan/s
kyagi
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@maximelabonne
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SkyAGI: Human Behavior Simulation in LLM Agents
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TANGO: Generate Sound Effects from Text Descriptions
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TANGO: Text to Audio using iNstruction-Guided diffusiOn Generate sound effects based on a text description. The code and model are available on GitHub. #AI https://
github.com/declare-lab/ta
ngo
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Efficient GPU Usage in AI Research Experiments
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I'm actually impressed they didn't spend more GPU hours in experiments. That's really efficient of them, especially considering the result.
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Integrating Bard AI into Google Colab for Enhanced Development
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It would be neat to have Bard directly integrated into Google Colab.
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Graph Neural Networks: Essential Resource Guide
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If you're interested in graph neural networks, I highly recommend checking it out. I'm confident that you'll find it to be an invaluable resource as you explore this exciting and rapidly growing field. Buy it now at
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Graph Neural Networks: Creating and Implementing GNNs with PyTorch
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The goal is to help you create, implement, and apply GNNs to solve real-world problems! You'll learn how to create graph datasets, implement GNNs using Python and #PyTorch Geometric (from @PyG_Team
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Graph Neural Networks: Book Recommendations and Advanced Applications
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And here is the final result with 5 recommended books and their respective scores. Code: https://
github.com/PacktPublishin
g/Hands-On-Graph-Neural-Networks-Using-Python/blob/main/Chapter17/chapter17.ipynb
… You'll find two other advanced use cases, related to time series forecasting and anomaly detection (the topic of my PhD!). -
GNN Guide: Building Recommender Systems with LightGCN
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This book is carefully crafted to provide a step-by-step guide for those new to the world of #GNN, while also offering advanced use cases and examples. For example, the following BookCrossing dataset is used to build a recommender system using LightGCN.
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Graph Neural Networks Hands-On Guide Released with Python Code
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I am thrilled to announce the release of my book, Hands-On Graph Neural Networks! It's been almost a year's worth of hard work, research, and collaboration with fellow experts in the field. The entire code is available on GitHub: https://
github.com/PacktPublishin
g/Hands-On-Graph-Neural-Networks-Using-Python
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Human Data vs Synthetic: Instructions Matter More Than Outputs
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This idea of generating data from seeds sounds wrong: it creates lower quality data than human conversations. I think a better approach would be to retro-engineer humanly written text to get instructions. The main question is are instructions more important than outputs? #llama