Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed. - GitHub - divamgupta/diffusionbee-stable-diffusion-ui: Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
In this tutorial you'll learn two methods you can use to perform real-time object detection using deep learning on the Raspberry Pi with OpenCV and Python.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA. Abstract: In this talk, we will survey how Deep Learning methods can be ap…
Deep Learning has revolutionised Pattern Recognition and Machine Learning. It is about credit assignment in adaptive systems with long chains of potentially causal links between actions and consequences.
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
Mahout currently has
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Random forest decision tree based classifier
High performance java collections (previously colt collections)
A vibrant community
and many more cool stuff to come by this summer thanks to Google summer of code
Platform for sharing and evaluation of intelligent algorithms. Data mining data, experiments, datasets, performance analysis, data repository, challenges. Research and applications, prediction. Data mining and machine learning
The Knowledge Discovery Machine Learning (KDML) group focuses on the neighboring subfields of computer science known as knowledge discovery in databases (KDD, sometimes referred to simply as data mining) and machine learning (ML). For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making. On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.
38. H-M. Haav, An Application of Inductive Concept Analysis to Construction of Domain-specific Ontologies, In: B. Thalheim, Gunar Fiedler (Eds), Emerging Database Research in East Europe, Proceedings of the Pre-conference Workshop of VLDB 2003, Computer Science Reports, Brandenburg University of Technology at Cottbus, 2003, 14/3, pp 63-67
K. Liu, B. Fang, und W. Zhang. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, Seite 1109--1118. New York, NY, USA, ACM, (2010)
P. Kluegl, M. Toepfer, F. Lemmerich, A. Hotho, und F. Puppe. Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics, (2013)
D. Krompass, M. Nickel, und V. Tresp. International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, October 30 - November 1, 2014, Seite 18--24. IEEE, (2014)