Sequence Modeling with Neural Networks - MIT 6.S191 (2018)
This article is a summary of a YouTube video "MIT 6.S191 (2018): Sequence Modeling with Neural Networks" by Alexander Amini
TLDR Recurrent neural networks (RNNs) and long short term memory cells (LSTMs) are effective tools for modeling sequences and capturing long-term dependencies, allowing for accurate predictions and analysis of various sequence prediction tasks.
Modeling sequences with neural networks allows for the understanding of complicated dependencies between data points, such as words in a sentence.
Considering the example of predicting the word in the sentence "In France, I had a great time and I learned some of the blank language," it becomes clear that looking back at the word "France" in the beginning of the sentence helps in predicting the word in the blank, highlighting the importance of considering a wider context in sequence modeling.
Recurrent neural networks (RNNs) are designed to address the challenges of dealing with variable length sequences, maintaining sequence order, tracking longer-term dependencies, and sharing parameters across the sequence.
The use of the same weight matrices also enables the RNN to handle variable length sequences, as it doesn't require separate parameters for every point in the sequence.
The self state in a recurrent neural network contains information from all past time steps, solving the issue of long-term dependencies.
LSTMs, or long short term memory cells, are able to effectively model longer-term dependencies by keeping memory within the cell state unchanged for many time steps.
Forgetting irrelevant parts of the cell state in sequence modeling is crucial, as it allows the model to focus on the current subject and make more accurate predictions for future words.
Recurrent neural networks can solve the vanishing gradient problem and are useful for modeling time series, waveforms, and other sequence prediction tasks like stock market trends or summarizing books or articles.