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CourseNatural Language ProcessingLast updated 4 days ago

Learn Deep Learning for Natural Language Processing

This course consists of ~10 tutorials to learn deep learning applied to natural language processing. You can think of this course as a "Free Online Nano Book". You'll learn core deep learning concepts and how to apply them in NLP applications such as language translation. Everything is 100% free.

Recurrent Neural Networks, the most popular Deep Learning architecture for Natural Language Processing

Natural language processing comprises of a set of computational techniques to understand natural languages such as English, Spanish, Chinese, etc. NLP techniques are used for search engine ranking, automatic translation, filtering spam email, etc. Recent advanced applications of NLP techniques have also started to be used for question answering, auto-replying and in digital assistants like Amazon Alexa and Google Assistant.

Introduction to Natural Language Processing

This section has just one tutorial, introducing you to NLP, discussing NLP applications, its challenges and various approaches to NLP.

Introduction to Natural Language Processing
13 mins

An Introduction to Deep Learning and Neural Networks

We start by reviewing what core concepts in machine learning. Then we discuss how deep learning differs from machine learning, i.e. what deep means and why it is important. We'll learn specifically what neural networks look like and how they are trained using back-propagation. We'll also introduce the concept of computational graphs which is how neural networks are implemented in popular deep learning libraries such as TensorFlow, Torch, etc. We'll end the section with learning regularization techniques specific to deep learning.

A Visual Introduction to Machine Learning
3 mins
What is deep learning? How does it relate to machine learning?
5 mins
A First Look at Neural Networks
9 mins
Computational Graphs and Backpropagation
11 mins
Regularization methods in Deep Learning
5 mins

Deep Natural Language Processing with LSTM Networks

We'll start by introducing Recurrent Neural Networks (RNN), which are neural networks with loops in them. We'll see how RNNs can be used for inputting and outputting sequences and how they maintain an internal state. Then we'll introduce Long Short-Term Memory (LSTM) networks and see how they overcome some significant shortcomings of general RNNs. We'll see how this model can be used for performing state-of-the-art language translation and along the way introduce the concept of word embeddings.

9 items.

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Introduction to Natural Language Processing

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