Deep Learning: Advanced NLP and RNNs
Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
What you’ll learn
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Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
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Build a neural machine translation system (can also be used for chatbots and question answering)
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Build a sequence-to-sequence (seq2seq) model
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Build an attention model
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Build a memory network (for question answering based on stories)
Requirements
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Understand what deep learning is for and how it is used
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Decent Python coding skills, especially tools for data science (Numpy, Matplotlib)
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Preferable to have experience with RNNs, LSTMs, and GRUs
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Preferable to have experience with Keras
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Preferable to understand word embeddings
Who this course is for:
- Students in machine learning, deep learning, artificial intelligence, and data science
- Professionals in machine learning, deep learning, artificial intelligence, and data science
- Anyone interested in state-of-the-art natural language processing
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