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!
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 / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
TIPS (for getting through the course):
USEFUL COURSE ORDERING:
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.
I am a data scientist, big data engineer, and full stack software engineer.
For my masters thesis I worked on brain-computer interfaces using machine learning. These assist non-verbal and non-mobile persons communicate with their family and caregivers.
I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing.
I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.