Algoliterary Bibliography: Difference between revisions
From Algolit
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*Hayles, Katherine N., 2016. ''Unthought. The Power of Cognitive Nonconscious'' | *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 | *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 | ||
====Software for Deep Learning and neural networks==== | ====Software for Deep Learning and neural networks==== | ||
Revision as of 17:49, 31 October 2017
These are the texts which surrounded us during the work sessions on natural language processing within machine learning.
Neural networks and 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/
Bias in 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
Neural networks and deep learning
- 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/
General background
- 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
Software for Deep Learning and neural networks
- Tensorflow (Google): https://www.tensorflow.org/
Tensorflow has become a major programming framework for neural networks. On the website a range of tutorials can be found to tackle machine learning problems, although for new users it can be useful to read some introductory literature on Tensorflow to grasp better how an algorithm is build up in Tensorflow. Good introductions are:
- 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)
Simulation tools
- Simulation of NN in browser: http://playground.tensorflow.org
- Nice introduction using this tool: https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground