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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

To mitigate these risks, the FL model uses personalized training algorithms and effective masking and parameterization before sharing information with the training coordinator. Solution overview We deploy FedML into multiple EKS clusters integrated with SageMaker for experiment tracking.

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How spaCy Works

Explosion

The short story is, there are no new killer algorithms. The way that the tokenizer works is novel and a bit neat, and the parser has a new feature set, but otherwise the key algorithms are well known in the recent literature. Part-of-speech Tagger In 2013, I wrote a blog post describing how to write a good part of speech tagger.

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Tuning Word2Vec with Bayesian Optimization: Applied to Music Recommendations

Towards AI

Word2Vec, a widely-adopted NLP algorithm has proven to be an efficient and valuable tool that is now applied across multiple domains, including recommendation systems. Songs that frequently co-occur or appear in similar contexts will have vector representations that are clustered closer together in the high-dimensional embedding space.

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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Supervised similarity: Learning symmetric relations from duplicate question data

Explosion

Without supervision, you’re stuck with whatever default relationship the unsupervised algorithm happens to recover. Alternatively, if you’re trying to cluster opinions in product reviews, the object is probably the decisive dimension. There’s no way for the algorithm to guess what you want, unless you tell it — with example data.

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A Deep Dive into Variational Autoencoders with PyTorch

PyImageSearch

VAEs were introduced in 2013 by Diederik et al. It serves as a direct drop-in replacement for the original Fashion-MNIST dataset for benchmarking machine learning algorithms, with the benefit of being more representative of the actual data tasks and challenges. in their paper Auto-Encoding Variational Bayes.

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Meet the winners of the Unsupervised Wisdom Challenge!

DrivenData Labs

Solvers submitted a wide range of methodologies to this end, including using open-source and third party LLMs (GPT, LLaMA), clustering (DBSCAN, K-Means), dimensionality reduction (PCA), topic modeling (LDA, BERT), sentence transformers, semantic search, named entity recognition, and more. and DistilBERT. What motivated you to participate? :