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Introduction to Real-Time Audio Programming in ChucK
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Learn to identify and debug neural network problems in NLP with Graham Neubig's material, covering training and test time issues. Workload: 1-2 hours.
Explore distributional semantics and word vectors with Graham Neubig's material, covering skip-grams, CBOW, and advanced methods for word vectors. Duration: 1-2 hours.
Explore attention in Neural Networks for NLP with Graham Neubig's 1-2 hour material, covering improvements, specialized varieties, and a case study.
Explore Neural Networks for NLP with Graham Neubig's 1-2 hour material, covering Encoder-Decoder Models, Conditional Generation, and more.
Learn efficiency tricks for Neural Networks in NLP from Graham Neubig's lecture. Topics include GPU training, parallel training, and softmax approximations. Workload: 1-2 hours.
Explore convolutional neural networks for text with Graham Neubig's online material. Learn about bag of words, context windows, sentence modeling, and more in under an hour.
Explore language modeling, neural networks, and optimization techniques with Graham Neubig's 1-2 hour material. Learn to prevent overfitting and measure model performance.
Explore advanced search algorithms like Beam Search and A* Search in NLP with this under 1-hour material by Graham Neubig.
Explore multi-task and multi-lingual learning in Neural Networks for NLP with Graham Neubig. Less than 1-hour workload, ideal for quick learning.
Explore neural networks for NLP with Graham Neubig's 1-2 hour material, covering chat-based and task-based dialog models, response generation, and dialog evaluation.
Explore Neural Networks for NLP with Graham Neubig's lecture, covering coreference models and discourse parsing. Less than 1-hour workload.
Explore unsupervised and semi-supervised learning methods in NLP with Graham Neubig's 1-2 hour material, covering design decisions for unsupervised models and examples of unsupervised learning.
Explore transition-based parsing in NLP with Graham Neubig's lecture, covering topics like shift-reduce parsing, stack LSTM, and linearized trees. Less than 1-hour workload.
Explore generative models, variational autoencoders, and latent random variables in NLP with this under 1-hour material by Graham Neubig.
Explore model interpretation in Neural Networks for NLP with Graham Neubig's material, covering themes like sentence embeddings, evaluation techniques, and future directions.
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