Natural Language Processing with Deep Learning in Python
Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets
What you’ll 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
- Use Gensim to obtain pretrained word vectors and compute similarities and analogies
- Install Numpy, Matplotlib, Sci-Kit Learn, and Theano or TensorFlow (should be extremely easy by now)
- Understand backpropagation and gradient descent, be able to derive and code the equations on your own
- Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
- Code a feedforward neural network in Theano (or Tensorflow)
- Helpful to have experience with tree algorithms
In this course we are going to look at NLP (natural language processing) with deep learning.
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
For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.
Who this course is for:
- 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.