Bibliographie Algolittéraire: Difference between revisions
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| − | Voici les textes qui nous ont | + | Voici les textes qui nous ont accompagnés lors nos journées de recherche sur le traitement du langage naturel dans l'apprentissage automatique. |
| + | |||
| + | ====Réseau neuronal et language==== | ||
| + | *CS224n: Natural Language Processing with Deep Learning (Stanford course) - http://web.stanford.edu/class/cs224n/syllabus.html | ||
| + | *''GloVe: Global Vectors for Word Representation'' - https://nlp.stanford.edu/projects/glove/ | ||
| + | *''The Unreasonable Effectiveness of Recurrent Neural Networks'' - http://karpathy.github.io/2015/05/21/rnn-effectiveness/ | ||
| + | |||
| + | ====Biais en language==== | ||
| + | *Caliskan, A., Bryson, J. J. and Narayanan, A., 2017. ''Semantics derived automatically from language corpora contain human-like biases.'' Science, 356 (6334), pp. 183-186. | ||
| + | *Bolukbasi, T., Chang, K. W., Zou, J. and Saligrama, V., 2016. ''Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.'' CoRR. | ||
| + | *Rob Speer, ''How to make a racist AI without really trying'' - https://gist.github.com/rspeer/ef750e7e407e04894cb3b78a82d66aed#file-how-to-make-a-racist-ai-without-really-trying-ipynb | ||
| + | |||
| + | ====Réseau neuronal et Aprrentissage profond==== | ||
| + | *Ian Goodfellow, Yoshua Bengio and Aaron Courville, ''Deep Learning'' - http://www.deeplearningbook.org/ | ||
| + | *Michael Nielsen, ''Neural Networks and Deep Learning'' - http://neuralnetworksanddeeplearning.com | ||
| + | |||
| + | *''Neural Networks, Manifolds, and Topology'' - http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ | ||
| + | *''Visualizing Representations: Deep Learning and Human Beings'' - http://colah.github.io/posts/2015-01-Visualizing-Representations/ | ||
| + | |||
| + | ====Contexte général==== | ||
| + | *Halpern, O., 2014. ''Beautiful Data: A History of Vision and Reason since 1945'', Duke Press - https://www.dukeupress.edu/beautiful-data, http://www.orithalpern.net/ | ||
| + | *Hayles, Katherine N., 2016. ''Unthought. The Power of Cognitive Nonconscious'' | ||
| + | *McKenzie, Adrian, 2015. ''The production of prediction: What does machine learning want?'' http://journals.sagepub.com/doi/abs/10.1177/1367549415577384?journalCode=ecsa | ||
| + | *Speech and Language Processing, Daniel Jurafsky and James H. Martin, Stanford University, 2017: https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf | ||
| + | |||
| + | ====Logiciels pour l'apprentissage profond et réseau nueronal==== | ||
| + | *Tensorflow (Google): https://www.tensorflow.org/ | ||
| + | Quelques introductions: | ||
| + | *Sam Abrahams, Danijar Hafner, Erik Erwitt, Ariel Scarpinelli, ''TensorFlow for Machine Intelligence. A Hands-On Introduction to Learning Algorithms.'' Bleeding Edge Press (2016) | ||
| + | *Rodolfo Bonnin, ''Building Machine Learning Projects with TensorFlow.'' Packt Publishing (2016) | ||
| + | *Nick McClure, ''TensorFlow Machine Learning Cookbook.'' Packt Publishing (2017) (more in-depth but also a slightly steeper learning curve) | ||
| + | * Scikit Learn: http://scikit-learn.org | ||
| + | |||
| + | ====Outils de simulation==== | ||
| + | *Simulation en navigateur: http://playground.tensorflow.org | ||
| + | *Introduction qui utilise TensorFlow: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground | ||
[[Category:Rencontres-Algolittéraires]] | [[Category:Rencontres-Algolittéraires]] | ||
Latest revision as of 15:31, 2 November 2017
Voici les textes qui nous ont accompagnés lors nos journées de recherche sur le traitement du langage naturel dans l'apprentissage automatique.
Contents
Réseau neuronal et language
- CS224n: Natural Language Processing with Deep Learning (Stanford course) - http://web.stanford.edu/class/cs224n/syllabus.html
- GloVe: Global Vectors for Word Representation - https://nlp.stanford.edu/projects/glove/
- The Unreasonable Effectiveness of Recurrent Neural Networks - http://karpathy.github.io/2015/05/21/rnn-effectiveness/
Biais en language
- Caliskan, A., Bryson, J. J. and Narayanan, A., 2017. Semantics derived automatically from language corpora contain human-like biases. Science, 356 (6334), pp. 183-186.
- Bolukbasi, T., Chang, K. W., Zou, J. and Saligrama, V., 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. CoRR.
- Rob Speer, How to make a racist AI without really trying - https://gist.github.com/rspeer/ef750e7e407e04894cb3b78a82d66aed#file-how-to-make-a-racist-ai-without-really-trying-ipynb
Réseau neuronal et Aprrentissage profond
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning - http://www.deeplearningbook.org/
- Michael Nielsen, Neural Networks and Deep Learning - http://neuralnetworksanddeeplearning.com
- Neural Networks, Manifolds, and Topology - http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/
- Visualizing Representations: Deep Learning and Human Beings - http://colah.github.io/posts/2015-01-Visualizing-Representations/
Contexte général
- Halpern, O., 2014. Beautiful Data: A History of Vision and Reason since 1945, Duke Press - https://www.dukeupress.edu/beautiful-data, http://www.orithalpern.net/
- Hayles, Katherine N., 2016. Unthought. The Power of Cognitive Nonconscious
- McKenzie, Adrian, 2015. The production of prediction: What does machine learning want? http://journals.sagepub.com/doi/abs/10.1177/1367549415577384?journalCode=ecsa
- Speech and Language Processing, Daniel Jurafsky and James H. Martin, Stanford University, 2017: https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf
Logiciels pour l'apprentissage profond et réseau nueronal
- Tensorflow (Google): https://www.tensorflow.org/
Quelques introductions:
- Sam Abrahams, Danijar Hafner, Erik Erwitt, Ariel Scarpinelli, TensorFlow for Machine Intelligence. A Hands-On Introduction to Learning Algorithms. Bleeding Edge Press (2016)
- Rodolfo Bonnin, Building Machine Learning Projects with TensorFlow. Packt Publishing (2016)
- Nick McClure, TensorFlow Machine Learning Cookbook. Packt Publishing (2017) (more in-depth but also a slightly steeper learning curve)
- Scikit Learn: http://scikit-learn.org
Outils de simulation
- Simulation en navigateur: http://playground.tensorflow.org
- Introduction qui utilise TensorFlow: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground