Uploaded March 2019 | Updated September 2026, 3 days ago
slides: mlvu.github.io/lectures/61.SequentialModels.annotated.pdf
course materials: mlvu.github.io
In the last lectures, we will discuss various deviations from the standard offline machine learning recipe we've discussed so far. Today: learning sequences. Language, music, time series and so on.
In the first half, we discuss the simple, but powerful approach of the Markov Model, and our first embedding model: word2vec.
In the second half, we discuss recurrent neural networks. A very powerful, but a bit more complex approach to dealing with sequences. Specifically, we focus on the LSTM; probably still the most power recurrent network available.
slides: mlvu.github.io/lectures/61.SequentialModels.annotated.pdf
course materials: mlvu.github.io
In the last lectures, we will discuss various deviations from the standard offline machine learning recipe we've discussed so far. Today: learning sequences. Language, music, time series and so on.
In the first half, we discuss the simple, but powerful approach of the Markov Model, and our first embedding model: word2vec.
In the second half, we discuss recurrent neural networks. A very powerful, but a bit more complex approach to dealing with sequences. Specifically, we focus on the LSTM; probably still the most power recurrent network available.
