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Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences
Instructor:
Lazy Programmer Inc.
16,826 students enrolled
English [Auto-generated] More
Understand the simple recurrent unit (Elman unit)
Understand the GRU (gated recurrent unit)
Understand the LSTM (long short-term memory unit)
Write various recurrent networks in Theano
Understand backpropagation through time
Understand how to mitigate the vanishing gradient problem
Solve the XOR and parity problems using a recurrent neural network
Use recurrent neural networks for language modeling
Use RNNs for generating text, like poetry
Visualize word embeddings and look for patterns in word vector representations

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models?

In the first section of the course we are going to add the concept of time to our neural networks.

I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit.

We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling.

You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used.

In this course, we are going to extend our language model so that it no longer makes the Markov assumption.

Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors.

In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance.

We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. 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:

  • calculus

  • linear algebra

  • probability (conditional and joint distributions)

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file

  • Deep learning: backpropagation, XOR problem

  • Can write a neural network in Theano and Tensorflow

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!

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Introduction and Outline

1
Outline of this Course
2
Review of Important Deep Learning Concepts
3
Where to get the Code and Data
4
How to Succeed in this Course

The Simple Recurrent Unit

1
Architecture of a Recurrent Unit
2
Prediction and Relationship to Markov Models
3
Unfolding a Recurrent Network
4
Backpropagation Through Time (BPTT)

We discuss how to do gradient descent when time is involved, pitfalls like the vanishing gradient problem and exploding gradient problem, the gradient clipping technique, and truncated backpropagation through time.

5
The Parity Problem - XOR on Steroids
6
The Parity Problem in Code using a Feedforward ANN
7
Theano Scan Tutorial
8
The Parity Problem in Code using a Recurrent Neural Network
9
On Adding Complexity

Recurrent Neural Networks for NLP

1
Word Embeddings and Recurrent Neural Networks
2
Word Analogies with Word Embeddings
3
Representing a sequence of words as a sequence of word embeddings
4
Generating Poetry
5
Generating Poetry in Code (part 1)
6
Generating Poetry in Code (part 2)
7
Classifying Poetry
8
Classifying Poetry in Code

Advanced RNN Units

1
Rated RNN Unit
2
RRNN in Code - Revisiting Poetry Generation
3
Gated Recurrent Unit (GRU)
4
GRU in Code
5
Long Short-Term Memory (LSTM)
6
LSTM in Code
7
Learning from Wikipedia Data
8
Alternative to Wikipedia Data: Brown Corpus
9
Learning from Wikipedia Data in Code (part 1)
10
Learning from Wikipedia Data in Code (part 2)
11
Visualizing the Word Embeddings

Batch Training

1
Batch Training for Simple RNN

TensorFlow

1
Simple RNN in TensorFlow

Basics Review

1
(Review) Theano Basics
2
(Review) Theano Neural Network in Code
3
(Review) Tensorflow Basics
4
(Review) Tensorflow Neural Network in Code

Appendix

1
What is the Appendix?
2
How to install wp2txt or WikiExtractor.py
3
Windows-Focused Environment Setup 2018
4
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
5
How to Code by Yourself (part 1)
6
How to Code by Yourself (part 2)
7
How to Succeed in this Course (Long Version)
8
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
9
Proof that using Jupyter Notebook is the same as not using it
10
BONUS: Where to get Udemy coupons and FREE deep learning material
11
Python 2 vs Python 3
12
Is Theano Dead?
13
What order should I take your courses in? (part 1)
14
What order should I take your courses in? (part 2)
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