awkward truth: engineering productivity is far more correlated with culture than team size there are startups w/ 10 engineers that ship more for their customers than big co engineering teams of 1000 (ex: Midjourney) you cannot throw people and/or money at building great things
AI
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DINOv2 Open-Source Release with Interactive Demo Available
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Want to explore DINOv2 for yourself? Today we're releasing both the open-source code and an interactive demo. GitHub https://
bit.ly/40eMPxk
Demo https://
bit.ly/3KHBnVa -
Meta Releases DINOv2: Self-Supervised Computer Vision Model
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Announced by Mark Zuckerberg this morning — today we're releasing DINOv2, the first method for training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards.
— AI at Meta (@AIatMeta) 17 avril 2023
More on this new work ➡️ https://t.co/h5exzLJsFt pic.twitter.com/2pdxdTyxC4Announced by Mark Zuckerberg this morning — today we're releasing DINOv2, the first method for training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards. More on this new work https://
bit.ly/3GQnIKf -
DINOv2 Complements Computer Vision Work Beyond Segmentation
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DINOv2 complements our other computer vision work, such as Segment Anything. While SAM is a promptable system focused on zero-shot generalization to diverse segmentation tasks, DINOv2 uses simple linear classifiers to achieve strong results across tasks beyond segmentation.
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AI Models Map Forests Tree-by-Tree Across Continents
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Models like this will be useful in a wide variety of applications. For example, we recently collaborated with @RestoreForward to use AI to map forests, tree-by-tree, across areas the size of continents. pic.twitter.com/T2we4cqTa4
— AI at Meta (@AIatMeta) 17 avril 2023Models like this will be useful in a wide variety of applications. For example, we recently collaborated with @RestoreForward to use AI to map forests, tree-by-tree, across areas the size of continents.
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Groq Celebrates Entanglement AI CEO for Quantum Computing Innovation
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Happy to see our customer @entanglement_ai CEO Jason Turner getting some well deserved attention for the space they are innovating in. #QuantumComputing #AI #machinelearning #inference #cybersecurity https://
reuters.com/article/quantu
m-software/waiting-for-quantum-computers-to-arrive-software-engineers-get-creative-idUSL1N36I0KA
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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: 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!). -
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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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.