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Natural Language Processing Nuggets: Getting Started with NLP

#artificialintelligence

This is a collection of some of my natural language processing (NLP) posts from the past year or so. They start from zero and progress accordingly, and are suitable for individuals looking to creep toward NLP and pick up some of the basic ideas, before hopefully branching out further (see the final 2 resources listed below for more on that). Not originally intended to be in any particular order, if you are inclined to read them all, they are best approached in the order they are presented. At the intersection of computational linguistics and artificial intelligence is where we find natural language processing. Very broadly, natural language processing (NLP) is a discipline which is interested in how human languages, and, to some extent, the humans who speak them, interact with technology.



7 Steps to Understanding Deep Learning

#artificialintelligence

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps! Deep learning is a branch of machine learning, employing numerous similar, yet distinct, deep neural network architectures to solve various problems in natural language processing, computer vision, and bioinformatics, among other fields. Deep learning has experienced a tremendous recent research resurgence, and has been shown to deliver state of the art results in numerous applications. In essence, deep learning is the implementation of neural networks with more than a single hidden layer of neurons.


How to Learn Data Science for Free

#artificialintelligence

The first part of the curriculum will focus on technical skills. I recommend learning these first so that you can take a practical first approach rather than say learning the mathematical theory first. Python is by far the most widely used programming language used for data science. In the Kaggle Machine Learning and Data Science survey carried out in 2018 83% of respondents said that they used Python on a daily basis. I would, therefore, recommend focusing on this language but also spending a little time on other languages such as R. Before you can start to use Python for data science you need a basic grasp of the fundamentals behind the language.


7 Steps to Understanding Deep Learning

@machinelearnbot

Deep learning is a branch of machine learning, employing numerous similar, yet distinct, deep neural network architectures to solve various problems in natural language processing, computer vision, and bioinformatics, among other fields. Deep learning has experienced a tremendous recent research resurgence, and has been shown to deliver state of the art results in numerous applications. In essence, deep learning is the implementation of neural networks with more than a single hidden layer of neurons. This is, however, a very simplistic view of deep learning, and not one that is unanimously agreed upon. These "deep" architectures also vary quite considerably, with different implementations being optimized for different tasks or goals.