Natural Language Processing with Deep Learning in Python
Natural Language Processing with Deep Learning in Python. Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets
120,00 € 10,00 €
The name of this course is Natural Language Processing with Deep Learning in Python. The knowledge you will get with this indescribable online course is astonishing. Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Lazy Programmer Inc., one of the very best experts in this field.
Description of this course: Natural Language Processing with Deep Learning in Python
Course 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 bagofwords and termdocument 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 – womanFrance – Paris = England – LondonDecember – 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 partsofspeech 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 bagofwords. 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! 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.Ask lots of questions on the discussion board. The more the better!Realize that most exercises will take you days or weeks to complete. USEFUL COURSE ORDERING: (The Numpy Stack in Python)Linear Regression in PythonLogistic Regression in Python(Supervised Machine Learning in Python)(Bayesian Machine Learning in Python: A/B Testing)Deep Learning in PythonPractical 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 PythonNatural Language Processing with Deep Learning in Python
Requirements of this course: Natural Language Processing with Deep Learning in Python
What are the requirements? Install Numpy, Matplotlib, SciKit 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
What will you learn in this course: Natural Language Processing with Deep Learning in Python?
What am I going to get from this course? Understand and implement word2vec Understand the CBOW method in word2vec Understand the skipgram 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 partsofspeech 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
Target audience of this course: Natural Language Processing with Deep Learning in Python
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 stateoftheart neural network architectures like recursive neural networks SHOULD NOT: Anyone who is not comfortable with the prerequisites.
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