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Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
The Dialog State Tracking Challenges 2 & 3 (DSTC2&3) were research challenge focused on improving the state of the art in tracking the state of spoken dialog systems. Transcription of spoken dialogs with labelling DSTC2 contains ~3.2k calls – DSTC3 contains ~2.3k calls Json Dialogue state tracking 2014 [74]
Fooocus is an open source generative artificial intelligence program that allows users to generate images from a text prompt. [3] [4] It uses Stable Diffusion as the base model for its image capabilities as well as a collection of default settings and prompts to make the image generation process more streamlined.
The competition was hosted on Kaggle, a service that runs data science competitions. It included two tracks: a hierarchical load forecasting track and a wind power forecasting track; both opened to contestants in September 2012.
SqueezeNet was originally described in SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. [1] AlexNet is a deep neural network that has 240 MB of parameters, and SqueezeNet has just 5 MB of parameters.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Exponential Random Graph Models (ERGMs) are a family of statistical models for analyzing data from social and other networks. [1] [2] Examples of networks examined using ERGM include knowledge networks, [3] organizational networks, [4] colleague networks, [5] social media networks, networks of scientific development, [6] and others.
It works on Linux, Windows, macOS, and is available in Python, [8] R, [9] and models built using CatBoost can be used for predictions in C++, Java, [10] C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub. [6] InfoWorld magazine awarded the library "The best machine learning tools" in 2017.