Package: deepNN 1.2
deepNN: Deep Learning
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Authors:
deepNN_1.2.tar.gz
deepNN_1.2.zip(r-4.5)deepNN_1.2.zip(r-4.4)deepNN_1.2.zip(r-4.3)
deepNN_1.2.tgz(r-4.4-any)deepNN_1.2.tgz(r-4.3-any)
deepNN_1.2.tar.gz(r-4.5-noble)deepNN_1.2.tar.gz(r-4.4-noble)
deepNN_1.2.tgz(r-4.4-emscripten)deepNN_1.2.tgz(r-4.3-emscripten)
deepNN.pdf |deepNN.html✨
deepNN/json (API)
| # Install 'deepNN' in R: |
| install.packages('deepNN', repos = c('https://bentaylor1.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:424b1fb71c. Checks:3 OK, 4 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 23 2025 |
| R-4.5-win | NOTE | Jan 23 2025 |
| R-4.5-linux | NOTE | Jan 23 2025 |
| R-4.4-win | NOTE | Jan 23 2025 |
| R-4.4-mac | NOTE | Jan 23 2025 |
| R-4.3-win | OK | Jan 23 2025 |
| R-4.3-mac | OK | Jan 23 2025 |
Exports:download_mnistdropoutProbshyptanidentL1_regularisationL2_regularisationlogisticmultinomialnbiasparnetworknnetparNNgrad_testNNpredictNNpredict.regressionno_regularisationQlossReLUsmoothReLUsoftmaxtrainwmultinomialwQloss
