Deep Learning: Advanced NLP and RNNs

Deep Learning: Advanced NLP and RNNs

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What you will learn

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)


Section 1: Welcome

Section 2: Review

Section 3: Bidirectional RNNs

Section 4: Sequence-to-sequence models (Seq2Seq)

Section 5: Attention

Section 6: Memory Networks

Section 7: Basics Review

Section 8: Appendix

Course Description

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks! 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. This course takes you to a higher systems level of thinking. Since you know how these things work, it’s time to build systems using these components. At the end of this course, you'll be able to build applications for problems like: text classification (examples are sentiment analysis and spam detection) neural machine translation question answering We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering. To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as: bidirectional RNNs seq2seq (sequence-to-sequence) attention memory networks All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. I am always available to answer your questions and help you along your data science journey. This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. See you in class! HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE: Decent Python coding skills Understand RNNs, CNNs, and word embeddings Know how to build, train, and evaluate a neural network in Keras TIPS (for getting through the course): Watch it at 2x. Take handwritten notes. This will drastically increase your ability to retain the information. Write down the equations. If you don't, I guarantee it will just look like gibberish. Ask lots of questions on the discussion board. The more the better! The best exercises will take you days or weeks to complete. Write code yourself, don't just sit there and look at my code. This is not a philosophy course! Who is the target audience? 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