A Review of the Neural History of Natural Language Processing
This is the first blog post in a two-part series. The series expands on the Frontiers of Natural Language Processing session organized by Herman Kamper and me at the Deep Learning Indaba 2018. Slides of the entire session can be found here. This post will discuss major recent advances in NLP focusing on neural network-based methods. The second post will discuss open problems in NLP.
What Is Deep Learning AI? A Simple Guide With 8 Practical Examples
There’s a lot of conversation lately about all the possibilities of machines learning to do things humans currently do in our factories, warehouses, offices and homes. While the technology is evolving – quickly – along with fears and excitement, terms such as artificial intelligence, machine learning and deep learning may leave you perplexed. I hope that this simple guide will help sort out the confusion around deep learning and that the 8 practical examples will help to clarify the actual use of deep learning technology today.1. Virtual assistants2. Translations3. Vision for driverless delivery trucks, drones and autonomous cars4. Chatbots and service bots5. Image colorization6. Facial recognition7. Medicine and pharmaceuticals8. Personalized shopping and entertainment
Teaching Machines Common Sense Reasoning
Today’s machine learning systems are more advanced than ever, capable of automating increasingly complex tasks and serving as a critical tool for human operators. Despite recent advances, however, a critical component of Artificial Intelligence (AI) remains just out of reach – machine common sense. Defined as ‘the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate,’ common sense forms a critical foundation for how humans interact with the world around them. Possessing this essential background knowledge could significantly advance the symbiotic partnership between humans and machines. But articulating and encoding this obscure-but-pervasive capability is no easy feat. ‘The absence of common sense prevents an intelligent system from understanding its world, communicating naturally with people, behaving reasonably in unforeseen situations, and learning from new experiences,’ said Dave Gunning, a program manager in DARPA’s Information Innovation Office (I2O). ‘This absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general AI applications we would like to create in the future.’
An NLP Approach to Mining Online Reviews using Topic Modeling (with Python codes)
E-commerce has revolutionized the way we shop. That phone you’ve been saving up to buy for months? It’s just a search and a few clicks away. Items are delivered within a matter of days (sometimes even the next day!). For online retailers, there are no constraints related to inventory management or space management They can sell as many different products as they want. Brick and mortar stores can keep only a limited number of products due to the finite space they have available. I remember when I used to place orders for books at my local bookstore, and it used to take over a week for the book to arrive. It seems like a story from the ancient times now!
How to Choose a Machine Learning Model – Some Guidelines
In this post, we explore some broad guidelines for selecting machine learning modelsThe overall steps for Machine Learning/Deep Learning are:• Collect data• Check for anomalies, missing data and clean the data• Perform statistical analysis and initial visualization• Build models• Check the accuracy• Present the resultsMachine learning tasks can be classified into• Supervised learning• Unsupervised learning• Semi-supervised learning• Reinforcement learning(In this document – we do not focus on the last two)Below are some approaches on choosing a model for Machine Learning/Deep Learning
Decorators in Python
In this tutorial, learn how to implement decorators in Python. A decorator is a design pattern in Python that allows a user to add new functionality to an existing object without modifying its structure. Decorators are usually called before the definition of a function you want to decorate. In this tutorial, we’ll show the reader how they can use decorators in their Python functions.
Understanding Confusion Matrix in R
This tutorial takes course material from DataCamp’s Machine Learning Toolbox course and allows you to practice confusion matrices in R.
Decision Trees and Random Forests in R
Decision trees are a highly useful visual aid in analyzing a series of predicted outcomes for a particular model. As such, it is often used as a supplement (or even alternative to) regression analysis in determining how a series of explanatory variables will impact the dependent variable.
Network Anomaly Detection Track Record in Real Life?
As I allude here, my long-held impression is that no true anomaly-based network IDS (NIDS) has ever been successful commercially and/or operationally. There were some bits of success, to be sure (‘OMG WE CAN DETECT PORTSCANS!!!’), but in total, they (IMHO) don’t quite measure up to SUCCESS of the approach. In light of this opinion, here is a fun question: do you think the current generation of machine learning (ML) – and ‘AI’-based (why is AI in quotes?) systems will work better? Note that I am aiming at a really, really low bar: will they work better than – per the above statement – not at all? But my definition of ‘work’ includes ‘work in today’s messy and evolving real life networks.’ This is actually a harder question than it seems. Of course, ML and ‘AI’ aficionados (who, as I am hearing, are generally saner compared to the blockchain types … these are more akin to clowns, really) would claim that of course ‘now with ML, things are totally different’, ‘because cyber AI’ and ‘next next next generation deep learning just works.’ On the other hand, some of the rumors we are hearing mention that in noisy, flat, poorly managed networks anomaly detection devolves to … no, really! … to signatures and fixed activity thresholds where humans write rules about what is bad and/or not good.
Dot-Pipe: an S3 Extensible Pipe for R
Pipe notation is popular with a large league of R users, with magrittr being the dominant realization. However, this should not be enough to consider piping in R as a completely settled topic that is not subject to further discussion, experiments, or the possibility of improvement. To promote innovation opportunities we describe wrapr ‘dot-pipe’, a well behaved sequencing operator with S3 extensibility. In this article we include a number of examples of using this pipe to interact with and extend other R packages.
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