Learning from Deep Learning: Difference between revisions
From Algolit
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| − | | | + | |Number of words: || 835.867 |
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| − | | | + | |Unique words: || 38.587 |
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| + | | Source: || [https://archive.org/details/DataScienceBookV3 An Introduction to Data Science, J Stanton], [https://deeplearning4j.org/neuralnet-overview.html Deep Learning: A Practitioner's Approach, O'Reilly media], [http://www.deeplearningbook.org/ Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville], [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning, Michael Nielsen], [http://www.heatonresearch.com/book/aifh-vol3-deep-neural.html Artificial Intelligence for Humans - Volume 3: Deep Learning and Neural Networks, Jeff Heaton], [http://www.apress.com/us/book/9781484228449 MatLab Deep Learning with Machine Learning - Neural Networks and Artificial Intelligence-Apress, Phil Kim], [http://www.springer.com/gp/book/9783319429984 Advances in Computer Vision and Pattern Recognition, Le Lu, Yefeng Zheng, Gustavo Carneiro, Lin Yang (eds.)] | ||
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| + | The ''Learning from Deep Learning'' dataset is an accumulation of 7 text books that give an technical explanation about deep learning. The books are all published in the last two years. This dataset was created to explore the effect of a technical practical language to the word2vec graphs. | ||
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| + | [[Category:Algoliterary-Encounters]] | ||
Latest revision as of 14:04, 2 November 2017
The Learning from Deep Learning dataset is an accumulation of 7 text books that give an technical explanation about deep learning. The books are all published in the last two years. This dataset was created to explore the effect of a technical practical language to the word2vec graphs.