On Education Deep Learning: Advanced NLP and RNNs - all courses

Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems) Build a neural machine translation system (can also be used for chatbots and question answering) Build a sequence-to-sequence (seq2seq) model Build an attention model Build a memory network (for question answering based on stories) Understand what deep learning is for and how it is used Decent Python coding skills, especially tools for data science (Numpy, Matplotlib) Preferable to have experience with RNNs, LSTMs, and GRUs Preferable to have experience with Keras Preferable to understand word embeddings It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.