Deep Learning: Advanced NLP and RNNs
4.7 (785 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,951 students enrolled

Deep Learning: Advanced NLP and RNNs

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
Highest Rated
4.7 (785 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
6,951 students enrolled
Last updated 11/2018
English
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Current price: $11.99 Original price: $199.99 Discount: 94% off
30-Day Money-Back Guarantee
This course includes
  • 8 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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)
Requirements
  • 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
Description

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 this course is for:
  • 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
Course content
Expand all 62 lectures 08:01:55
+ Review
13 lectures 01:55:19
Review Section Introduction
04:24
What is a word embedding?
15:10
Using word embeddings
04:33
What is a CNN?
13:36
Where to get the data
05:06
CNN Code (part 1)
15:08
CNN Code (part 2)
06:14
What is an RNN?
13:11
GRUs and LSTMs
10:47
Different Types of RNN Tasks
12:27
A Simple RNN Experiment
06:29
RNN Code
03:25
Review Section Summary
04:49
+ Bidirectional RNNs
6 lectures 30:46
Bidirectional RNNs Motivation
08:31
Bidirectional RNN Experiment
05:09
Bidirectional RNN Code
02:33
Image Classification with Bidirectional RNNs
06:12
Image Classification Code
05:45
Bidirectional RNNs Section Summary
02:36
+ Sequence-to-sequence models (Seq2Seq)
9 lectures 52:51
Seq2Seq Theory
07:29
Seq2Seq Applications
03:27
Decoding in Detail and Teacher Forcing
06:47
Poetry Revisited
03:28
Poetry Revisited Code 1
08:29
Poetry Revisited Code 2
06:58
Seq2Seq in Code 1
07:55
Seq2Seq in Code 2
05:14
Seq2Seq Section Summary
03:04
+ Attention
9 lectures 01:04:10
Attention Section Introduction
02:28
Attention Theory
18:04
Teacher Forcing
02:09
Helpful Implementation Details
11:21
Attention Code 1
09:48
Attention Code 2
03:50
Visualizing Attention
02:26
Building a Chatbot without any more Code
10:31
Attention Section Summary
03:33
+ Memory Networks
6 lectures 40:45
Memory Networks Section Introduction
09:19
Memory Networks Theory
08:55
Memory Networks Code 1
07:55
Memory Networks Code 2
05:05
Memory Networks Code 3
05:41
Memory Networks Section Summary
03:50
+ Basics Review
3 lectures 17:51
(Review) Keras Discussion
06:48
(Review) Keras Neural Network in Code
06:37
(Review) Keras Functional API
04:26
+ Appendix
12 lectures 02:25:15
What is the Appendix?
02:48
Windows-Focused Environment Setup 2018
20:20
How to How to install Numpy, Theano, Tensorflow, etc...
17:30
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
How to Succeed in this Course (Long Version)
10:24
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
Python 2 vs Python 3
04:38
BONUS: Where to get discount coupons and FREE deep learning material
02:20