Deep Learning for Natural Language Processing
Artificial Intelligence is one of the most interesting fields of research today, and language is the most compelling manifestation of intelligence.
• Linear Algebra,
• Calculus,
• Probability.
• Evaluating ML models
(train/validation/test split, cross validation etc.),
• overfitting, generalisation, and regularisation, • optimisation
(objective functions, stochastic gradient descent), • linear regression and classification, neural networks
(common non-linearities, backpropagations etc.).
• Evaluating ML models
(train/validation/test split, cross validation etc.),
• overfitting, generalisation, and regularisation, • optimisation
(objective functions, stochastic gradient descent), • linear regression and classification, neural networks
(common non-linearities, backpropagations etc.).
Knowledge of, or ability to learn quickly, a NN toolkit (e.g. Torch, TensorFlow, Theano, DyNet etc.)
1. Introduction Phil Blunsom (Oxford and DM) and Wang Ling (DM)
2. Lexical Semantics Ed Grefenstette (DM)
3. RNNs
4. Language Modelling Phil Blunsom
5. Text Classification Karl Moritz Hermann (DM)
6. RNNs and GPUs Jeremy Appleyard (nvidia)
7&8. Sequence Transduction Chris Dyer (CMU and DM)
9&10. Speech Andrew Senior (DM)
11. Question Answering Karl Moritz Hermann
12. Memory Ed Grefenstette
13. Linguistic Structure Chris Dyer
14. Conclusion Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
Phil Blunsom
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