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- Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.
- So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models?
- We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem - you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.
- In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks - language modeling.
- Another popular application of neural networks for language is word vectors or word embeddings.

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