### Natural Language Processing with Deep Learning in Python

Category: Tutorial

\$120 | Duration: 5 hours | Video: h264, 1280x720 | Audio: AAC, 44100 Hz, 2 Ch | 665 MB
Genre: eLearning | Language: English | Project Files

What Will I Learn?

Understand and implement word2vec
Understand the CBOW method in word2vec
Understand the skip-gram method in word2vec
Understand the negative sampling optimization in word2vec
Understand and implement GLoVe using gradient descent and alternating least squares
Use recurrent neural networks for parts-of-speech tagging
Use recurrent neural networks for named entity recognition
Understand and implement recursive neural networks for sentiment analysis
Understand and implement recursive neural tensor networks for sentiment analysis

Requirements

Install Numpy, MatDescriptionlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)
Understand backpropagation and gradient descent, be able to do it on your own.
Code a recurrent neural network in Theano
Code a feedforward neural network in Theano

Description

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 I'm going to show you how to do even more awesome things. We'll learn not just 1, but 4 new architectures in this course.

First up is word2vec.

In this course, I'm going to show you exactly how word2vec works, from theory to implementation, and you'll see that it's 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 it's 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. You'll see that just about any problem can be solved using neural networks, but you'll also learn the dangers of having too much complexity.

Lastly, you'll 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.

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!

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.

USEFUL COURSE ORDERING:

(The Numpy Stack in Python)
Linear Regression in Python
Logistic Regression in Python
(Supervised Machine Learning in Python)
(Bayesian Machine Learning in Python: A/B Testing)
Deep Learning in Python
Practical Deep Learning in Theano and TensorFlow
(Supervised Machine Learning in Python 2: Ensemble Methods)
Convolutional Neural Networks in Python
(Easy NLP)
(Cluster Analysis and Unsupervised Machine Learning)
Unsupervised Deep Learning
(Hidden Markov Models)
Recurrent Neural Networks in Python
Artificial Intelligence: Reinforcement Learning in Python
Natural Language Processing with Deep Learning in Python

HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:

calculus
linear algebra
probability (conditional and joint distributions)
Python coding: if/else, loops, lists, dicts, sets
neural networks and backpropagation
Can write a feedforward neural network in Theano and TensorFlow
Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow

Who is the target audience?
Students and professionals who want to create word vector representations for various NLP tasks
Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
SHOULD NOT: Anyone who is not comfortable with the prerequisites.
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Screenshots

Screenshots

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