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: 77
Length: 9h 19m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

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".

Testimonials and Success Stories


I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Welcome to Advanced NLP and RNNs

4 Lectures · 14min
  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 (03:04)

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 (03:10)

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)

Attention

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)

Course Conclusion

1 Lectures · 03min
  1. What to Learn Next (03:59)

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)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. 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 (05:49)

Extras

  • Speech Recognition
  • Stock Prediction Notebooks
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