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Supports teaching, studying or skills practice with AI-guided learning tools.
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If this is your first time building with Generative AI models, check out our course, which includes 21 lessons on building with GenAI.
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Large scale Gaussian Mixture Models.
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A collection of functions for mathematical and statistical computing, macine learning, etc., wrapping several JVM libraries.
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High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
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A fast Evolution Strategy implementation in Python.
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Easy-to-use Federated Learning Platform.
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Ready to use deeplearning docker images.
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Clojure wrapper for Deeplearning4j.
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Julia module for Distance evaluation.
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Deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.
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Decision Tree Classifier and Regressor.
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Dataframes for machine-learning and statistics (similar to pandas).
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Microsoft are pleased to offer a 10-week, 20-lesson curriculum all about Data Science.
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Supports teaching, studying or skills practice with AI-guided learning tools.
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Torch CUDA Neural Network Implementation.
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FANN (Fast Artificial Neural Network) binding.
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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai.
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Neural networks, regression and feature learning in Clojure.
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Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms.
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Basic functions for clustering data: k-means, dp-means, etc.
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Multidimensional cluster generation in R.
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Multidimensional cluster generation in Julia.
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Implementation of Random Forest in Common Lisp.