Natural Language Processing with Deep Learning in Python

Course details

In this course we are going to look at advanced NLP.

Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.

These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.

In this course Im going to show you how to do even more awesome things. Well learn not just 1, but 4 new architectures in this course.

First up isword2vec.

In this course, Im going to show you exactly how word2vec works, from theory to implementation, and youll see that its merely the application of skills you already know.

Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:

• king - man = queen - woman
• France - Paris = England - London
• December - Novemeber = July - June

We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.

Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and its way easier to train.

We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. Youll see that just about any problem can be solved using neural networks, but youll also learn the dangers of having too much complexity.

Lastly, youll learn about recursive neural networks,which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.

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: nlp_class2

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

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.

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
• neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own
• Can write a feedforward neural network in Theano and TensorFlow
• Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow from basic primitives, especially the scan function
• Helpful to have experience with tree algorithms

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)