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  2. Keras - Wikipedia

    en.wikipedia.org/wiki/Keras

    Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...

  3. Think aloud protocol - Wikipedia

    en.wikipedia.org/wiki/Think_aloud_protocol

    Ask open-ended questions and follow-up questions. The team should avoid asking leading questions or giving clues. Analyze the findings and summarize insights: The team should use notes taken during the sessions to generate insights and to find common patterns. Based on the findings, the design team could then decide directions to take action on.

  4. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    Keras: François Chollet 2015 MIT license: Yes Linux, macOS, Windows: Python: Python, R: Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks: 1992 Proprietary: No Linux, macOS, Windows: C, C++ ...

  5. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    [5] [6] It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google 's internal use in research and production. [ 7 ] [ 8 ] [ 9 ] The initial version was released under the Apache License 2.0 in 2015.

  6. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    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.

  7. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Neural networks are typically trained through empirical risk minimization.This method is based on the idea of optimizing the network's parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset. [4]

  8. François Chollet - Wikipedia

    en.wikipedia.org/wiki/François_Chollet

    Chollet is the creator of the Keras deep-learning library, released in 2015. His research focuses on computer vision , the application of machine learning to formal reasoning , abstraction , [ 2 ] and how to achieve greater generality in artificial intelligence .

  9. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    Suppose, to address the question of gender discrimination, we have survey data on salaries within a particular field, e.g., computer software. It is known women are under-represented considerably in a random sample of software engineers, which would be important when adjusting for other variables such as years employed and current level of ...