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.
Instructor Details
Courses : 22
Specification: Natural Language Processing with Deep Learning in Python
|
6 reviews for Natural Language Processing with Deep Learning in Python
Add a review Cancel reply
This site uses Akismet to reduce spam. Learn how your comment data is processed.
$39.99 $9.99
Roberto Mu oz Soria –
The instructor knows a lot about deep learning and the course content is well organized
Andre Luis Costa Carvalho –
The explanation is excellent, but the QA doesn’t work and at the end of the sections the results are not discussed.
Octavio Urbina Diaz –
Good explanations.The pace of the presentation is good
Dipakkumar Rameshbhai Mohnani –
Sorry I was not satisfied
Nima Safaei –
Well, material is good, but I do not like the way the instructor describes the codes.
Tobias Hentrich –
Die Art der Pr sentation ist mitrei end und ich kann dem Vortrag gut folgen. Der grundlegende Ansatz Theory >Code empfinde ich als w nschenswert, da ich Dinge lieber erst im Grundsatz verstehe, bevor ich eine Umsetzung angehe.