State-of-the-art count-based word embeddings for low-resource languages with a special focus on historical languages.
Sequence Models coding assignments
Course materials for "Meaningful Text Analysis with Word Embeddings," taught at the Digital Humanities Summer Institute, June 2021.
a non-neural network approach for word embedding
Includes projects and assignment from Sequence Model course from Deep Learning Specialization from Coursera.Includes works on how to build a RNN with only python function and without using any framework. Other projects such as LSTM concepts & implementation, Machine translation, trigger word detection, word vector representation etc
Systems that can visualize word embedding vectors in 3D and 2D spaces.
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