Deep Learning: Advanced NLP and RNNs

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

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Course Data

Lectures: 75
Length: 9h 13m
Skill Level: All Levels
Languages: English
Includes: Lifetime access

Course Description

It’s hard to believe it's 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

In the bonus section, we'll be looking at speech recognition using Deep Learning.

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!

Suggested Prerequisites:

  • Decent Python coding skills
  • Understand RNNs, CNNs, and word embeddings
  • Know how to build, train, and evaluate a neural network in Keras

Tips for success:

  • Use the video speed changer! Personally, I like to watch 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!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".


Welcome to Advanced NLP and RNNs

4 Lectures · 17min
  1. Introduction (02:51) (FREE preview available)
  2. Outline (04:09)
  3. Where to get the code (04:45)
  4. How to succeed in this course (05:52)

Recurrent Neural Networks, Convolutional Neural Networks, and Word Embeddings

15 Lectures · 02hr 00min
  1. Review Section Introduction (04:24)
  2. How to Open Files for Windows Users (02:18)
  3. What is a word embedding? (15:10)
  4. Using word embeddings (04:33)
  5. What is a CNN? (13:36)
  6. Where to get the data (05:06)
  7. CNN Code (part 1) (15:08)
  8. CNN Code (part 2) (06:14)
  9. What is an RNN? (13:11)
  10. GRUs and LSTMs (10:47)
  11. Different Types of RNN Tasks (12:27)
  12. A Simple RNN Experiment (06:29)
  13. RNN Code (03:25)
  14. Review Section Summary (04:49)
  15. Suggestion Box (03:03)

Bidirectional RNNs

6 Lectures · 30min
  1. Bidirectional RNNs Motivation (08:31)
  2. Bidirectional RNN Experiment (05:09)
  3. Bidirectional RNN Code (02:33)
  4. Image Classification with Bidirectional RNNs (06:12)
  5. Image Classification Code (05:45)
  6. Bidirectional RNNs Section Summary (02:36)

Sequence-to-sequence models (Seq2Seq)

9 Lectures · 52min
  1. Seq2Seq Theory (07:29)
  2. Seq2Seq Applications (03:27)
  3. Decoding in Detail and Teacher Forcing (06:47)
  4. Poetry Revisited (03:28)
  5. Poetry Revisited Code 1 (08:29)
  6. Poetry Revisited Code 2 (06:58)
  7. Seq2Seq in Code 1 (07:55)
  8. Seq2Seq in Code 2 (05:14)
  9. Seq2Seq Section Summary (03:04)


9 Lectures · 01hr 04min
  1. Attention Section Introduction (02:28)
  2. Attention Theory (18:04)
  3. Teacher Forcing (02:09)
  4. Helpful Implementation Details (11:21)
  5. Attention Code 1 (09:48)
  6. Attention Code 2 (03:50)
  7. Visualizing Attention (02:26)
  8. Building a Chatbot without any more Code (10:31)
  9. Attention Section Summary (03:33)

Memory Networks

6 Lectures · 40min
  1. Memory Networks Section Introduction (09:19)
  2. Memory Networks Theory (08:55)
  3. Memory Networks Code 1 (07:55)
  4. Memory Networks Code 2 (05:05)
  5. Memory Networks Code 3 (05:41)
  6. Memory Networks Section Summary (03:50)

Stock Predictions

10 Lectures · 58min
  1. Stock Predictions Section Introduction (04:51)
  2. Making the Dataset (05:19)
  3. Forecasting (07:29)
  4. A Simple Time Series (09:58)
  5. Naive Forecast (08:27)
  6. Stock Prediction (pt 1) (03:45)
  7. Stock Prediction (pt 2) (06:04)
  8. Stock Prediction (pt 3) (04:50)
  9. Stock Prediction (pt 4) (02:02)
  10. Stock Predictions Section Summary (05:35)

Basics Review

4 Lectures · 19min
  1. Keras Discussion (06:49)
  2. Keras Neural Network in Code (06:38)
  3. Keras Functional API (04:27)
  4. How to easily convert Keras into Tensorflow 2.0 code (01:49)

Setting Up Your Environment (Appendix/FAQ by Student Request)

2 Lectures · 37min
  1. Windows-Focused Environment Setup 2018 (20:21)
  2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

4 Lectures · 42min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:31)


  • Speech Recognition
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