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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. | + | 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]] | | [[Category:Algoliterary-Encounters]] |
Latest revision as of 14:04, 2 November 2017
| Type: |
Dataset
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| Number of words: |
835.867
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| Unique words: |
38.587
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| Source: |
An Introduction to Data Science, J Stanton, Deep Learning: A Practitioner's Approach, O'Reilly media, Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, Neural Networks and Deep Learning, Michael Nielsen, Artificial Intelligence for Humans - Volume 3: Deep Learning and Neural Networks, Jeff Heaton, MatLab Deep Learning with Machine Learning - Neural Networks and Artificial Intelligence-Apress, Phil Kim, 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.