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.
Being highly enthusiastic about research in deep learning I was always searching for unexplored areas in the field (Though it is tough to find one). I had previously worked on Maths word problem…
In the previous blog I explained the theory behind and how a Convolutional Neural Network works for a classification task. Here I will go a step further and touch on techniques used for object…
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…
I’ve always been curious about what makes someone “look” male or female, probably because I’m female but have never looked conventionally feminine. I was a tomboy as a child and remained one as an…
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.
The aim of this project is to produce age-appropriate non-fiction books for children from birth to age 12. These books are richly illustrated with photographs, diagrams, sketches, and original drawings. Wikijunior books are produced by a worldwide community of writers, teachers, students, and young people all working together. The books present factual information that is verifiable. You are invited to join in and write, edit, and rewrite each module and book to improve its content. Our books are distributed free of charge under the terms of the Creative Commons Attribution-ShareAlike License.
Short introduction to Vector Space Model (VSM) In information retrieval or text mining, the term frequency - inverse document frequency also called tf-idf, is
This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
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.
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
M. Ferrari Dacrema, P. Cremonesi, and D. Jannach. Proceedings of the 13th ACM Conference on Recommender Systems, page 101–109. New York, NY, USA, Association for Computing Machinery, (2019)
J. Lin, R. Nogueira, and A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.