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Deep Learning: Advanced NLP and RNNs


Deep Learning: Advanced NLP and RNNs


Deep Learning: Advanced NLP and RNNs Over the past year, many of you have been asking for a followup on my RNN and Deep NLP courses. I am glad to announce that today, that course is here.
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What you'll 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)


  • 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.

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.

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