Deep Learning: Recurrent Neural Networks in Python

Course details

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 weve seen on tasks that we havent made progress on in decades.

So whats 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 oftime to our neural networks.

Ill introduce you to the Simple Recurrent Unit, also known as the Elman unit.

We are going to revisit the XOR problem, but were going to extend it so that it becomes the parity problem - youll 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 didnt 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 Ill show you how recurrent neural networks can also be used for creating word vectors.

In the section after, well 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.

Well 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 inNumpy,Matplotlib,andTheano. 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 wantmorethan just a superficial look at machine learning models, this course is for you.

See you in class!

NOTES:

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: rnn_class

Make sure you always "git pull" so you have the latest version!

HARD PREREQUISITES /KNOWLEDGEYOUARE ASSUMEDTOHAVE:

• 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.

WHATORDERSHOULDITAKEYOURCOURSESIN?:

• 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)