GitHub - mhuckvale/pals0039: Introduction to deep learning for speech and language processing

These pages contain learning resources and laboratory exercises for the module: Introduction to Deep Learning for Speech and Language Processing, delivered by Division of Psychology and Language Sciences, University College London.

Tutorials

Demonstrations

RNN Demo - Demonstration of Recurrent Neural Networks
LM Demo - Demonstration of Language Modelling
Seq2Seq Demo - Demonstration of Sequence-to-sequence processing

Exercises

Exercise 1.1 - Python language exercises [Answers]
Exercise 1.2 - Numpy exercises [Answers]
Exercise 1.3 - Matplotlib exercises [Answers]
Exercise 2.1 - Pandas exercises [Answers]
Exercise 2.2 - Regression problem [Answers]
Exercise 2.3 - Classification problem [Answers]
Exercise 3.1 - Gradient descent [Answers]
Exercise 3.2 - Autoencoder [Answers]
Exercise 3.3 - Vowel classifier [Answers]
Exercise 4.1 - Sentiment analysis [Answers]
Exercise 4.2 - Emotion classification [Answers]
Exercise 4.3 - Prediction of speaker age [Answers]
Exercise 5.1 - Word similarity in Wordnet [Answers]
Exercise 5.2 - Training word embeddings [Answers]
Exercise 5.3 - Using word embeddings for tagging [Answers]
Exercise 6.1 - RNN for sentiment analysis [Answers]
Exercise 6.2 - Isolated word speech recogniser [Answers]
Exercise 6.3 - Calculation of phone posteriors [Answers]
Exercise 7.1 - Character-level language model [Answers]
Exercise 7.2 - Cloze task predictor using word language model [Answers]
Exercise 8.1 - Grapheme to phoneme conversion [Answers]
Exercise 8.2 - Sentence embedding encoder [Answers]
Exercise 8.3 - Neural machine translation [Answers]
Exercise 9.1 - Movie dialogue chatbot [Answers]

To open one of these notebooks in Colaboratory or Binder,

Name		Name	Last commit message	Last commit date
Latest commit History 250 Commits
Answers_0.ipynb		Answers_0.ipynb
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Answers_1_3.ipynb		Answers_1_3.ipynb
Answers_2_1.ipynb		Answers_2_1.ipynb
Answers_2_2.ipynb		Answers_2_2.ipynb
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Answers_3_3.ipynb		Answers_3_3.ipynb
Answers_4_1.ipynb		Answers_4_1.ipynb
Answers_4_2.ipynb		Answers_4_2.ipynb
Answers_4_3.ipynb		Answers_4_3.ipynb
Answers_5_1.ipynb		Answers_5_1.ipynb
Answers_5_2.ipynb		Answers_5_2.ipynb
Answers_5_3.ipynb		Answers_5_3.ipynb
Answers_6_1.ipynb		Answers_6_1.ipynb
Answers_6_2.ipynb		Answers_6_2.ipynb
Answers_6_3.ipynb		Answers_6_3.ipynb
Answers_7_1.ipynb		Answers_7_1.ipynb
Answers_7_2.ipynb		Answers_7_2.ipynb
Answers_8_1.ipynb		Answers_8_1.ipynb
Answers_8_2.ipynb		Answers_8_2.ipynb
Answers_8_3.ipynb		Answers_8_3.ipynb
Answers_9_1.ipynb		Answers_9_1.ipynb
Demo_LM.ipynb		Demo_LM.ipynb
Demo_RNN.ipynb		Demo_RNN.ipynb
Demo_Seq2Seq.ipynb		Demo_Seq2Seq.ipynb
Exercise_0_1.ipynb		Exercise_0_1.ipynb
Exercise_0_2.ipynb		Exercise_0_2.ipynb
Exercise_0_3.ipynb		Exercise_0_3.ipynb
Exercise_1_1.ipynb		Exercise_1_1.ipynb
Exercise_1_2.ipynb		Exercise_1_2.ipynb
Exercise_1_3.ipynb		Exercise_1_3.ipynb
Exercise_2_1.ipynb		Exercise_2_1.ipynb
Exercise_2_2.ipynb		Exercise_2_2.ipynb
Exercise_2_3.ipynb		Exercise_2_3.ipynb
Exercise_3_1.ipynb		Exercise_3_1.ipynb
Exercise_3_2.ipynb		Exercise_3_2.ipynb
Exercise_3_3.ipynb		Exercise_3_3.ipynb
Exercise_4_1.ipynb		Exercise_4_1.ipynb
Exercise_4_2.ipynb		Exercise_4_2.ipynb
Exercise_4_3.ipynb		Exercise_4_3.ipynb
Exercise_5_1.ipynb		Exercise_5_1.ipynb
Exercise_5_2.ipynb		Exercise_5_2.ipynb
Exercise_5_3.ipynb		Exercise_5_3.ipynb
Exercise_6_1.ipynb		Exercise_6_1.ipynb
Exercise_6_2.ipynb		Exercise_6_2.ipynb
Exercise_6_3.ipynb		Exercise_6_3.ipynb
Exercise_7_1.ipynb		Exercise_7_1.ipynb
Exercise_7_2.ipynb		Exercise_7_2.ipynb
Exercise_8_1.ipynb		Exercise_8_1.ipynb
Exercise_8_2.ipynb		Exercise_8_2.ipynb
Exercise_8_3.ipynb		Exercise_8_3.ipynb
Exercise_9_1.ipynb		Exercise_9_1.ipynb
README.md		README.md
Tutorial_Matplotlib.ipynb		Tutorial_Matplotlib.ipynb
Tutorial_NLTK.ipynb		Tutorial_NLTK.ipynb
Tutorial_Numpy.ipynb		Tutorial_Numpy.ipynb
Tutorial_Pandas.ipynb		Tutorial_Pandas.ipynb
Tutorial_Python.ipynb		Tutorial_Python.ipynb
environment.yml		environment.yml

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