This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.
– Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
– Be able to apply sequence models to natural language problems, including text synthesis.
– Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
This is the fifth and final course of the Deep Learning Specialization.
Who is this class for:
– Learners that took course one, two, and four of the specialization. Course three is also recommended.
– Anyone that already has a solid understanding of learning with neural networks, including convolutional networks, and wants to learn how to develop recurrent neural networks.
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