classification


Comparison of Deepnet & Neuralnet

@machinelearnbot

Based on two R packages for neural networks. In this article, I compare two available R packages for using neural networks to model data: neuralnet and deepnet. Through the comparisons I highlight various challenges in finding good hyperparameter values. I show that some needed hyperparameters differ when using these two packages, even with the same underlying algorithmic approach. Both packages can be obtained via the R CRAN repository (see links at the end).


Confused by data visualization? Here's how to cope in a world of many features - Dataconomy

@machinelearnbot

Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data. Now more than ever, data are collected from every aspect of our lives. From social media and advertising to artificial intelligence and automated systems, understanding and parsing information have become highly valuable skills. But we often overlook the importance of knowing how to communicate data to peers and to the public in an effective, meaningful way. The first tools that come to mind in considering how to best communicate data – especially statistics – are graphs and scatter plots.


The Data Science Process With Azure Machine Learning

@machinelearnbot

It's no secret today that all our applications and devices are generating tons of data; thus making data analytics a very hot topic. Microsoft Azure has all the tools necessary to ingest, manage, and process all this data, which is also known as Big Data. However, all this data in and of itself is not useful unless processed, interpreted, and visualized correctly. Another power behind the data acquired through the years is to make Predictive Analytics, that is, using the data to make forecasts and predictions. But, by only using the data gathered, it is difficult to make an analysis.


Talking Code - December 2017 php[architect] magazine:

@machinelearnbot

Chatbots are currently experiencing a rapid increase in popularity, with millions of people regularly using messaging platforms such as Facebook Messenger and Slack. In this article, I'm going to explain why chatbots are important, and how we as PHP developers can become part of the robot revolution. Hands-free is the future of internet services due to artificial intelligence (AI). AI is a branch of Computer Science which investigates and creates intelligent machines and software. Amazon Alexa is leading the charge to a hands-free future.


Decision Trees in Machine Learning – Towards Data Science

#artificialintelligence

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. As the name goes, it uses a tree-like model of decisions. Though a commonly used tool in data mining for deriving a strategy to reach a particular goal, its also widely used in machine learning, which will be the main focus of this article. For this let's consider a very basic example that uses titanic data set for predicting whether a passenger will survive or not.


Machine learning: Supervised methods (PDF Download Available)

#artificialintelligence

We'll illustrate SVM using a two-class problem and begin with Typically, C is chosen using cross-validation2. Points at the margin's edge (black outlines) are called The margin is now 0.64 with six support vectors. AU: the title is long and a bit clunky. What do you think about deleting'supervised methods' from it?


apple/turicreate

#artificialintelligence

Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app. It's easy to use the resulting model in an iOS application: For detailed instructions for different varieties of Linux see LINUX_INSTALL.md. For common installation issues see INSTALL_ISSUES.md. We recommend using virtualenv to use, install, or build Turi Create.


Understanding SSD MultiBox -- Real-Time Object Detection In Deep Learning

@machinelearnbot

Since AlexNet took the research world by storm at the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), deep learning has become the go-to method for image recognition tasks, far surpassing more traditional computer vision methods used in the literature. In the field of computer vision, convolution neural networks excel at image classification, which consists of categorising images, given a set of classes (e.g. Nowadays, deep learning networks are better at image classification than humans, which shows just how powerful this technique is. However, we as humans do far more than just classify images when observing and interacting with the world. We also localize and classify each element within our field of view.


Machine Learning – Can We Please Just Agree What This Means

@machinelearnbot

Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. "Machine Learning" is just the most recent case in point. It's had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Let's make up our minds. As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives.


Fruit of an acquisition: Apple AI software goes open

#artificialintelligence

Apple's joined other juggernauts of the tech sector by releasing an open source AI framework. Turi Create 4.0, which landed at GitHub recently, is a fruit of its 2016 US$200 million acquisition of Turi. As the GitHub description explains, it targets app developers that want custom machine learning models but don't have the expertise to "add recommendations, object detection, image classification, image similarity or activity classification" to their apps. Completed models are exported to Core ML for use in "iOS, macOS, watchOS, and tvOS apps". Other details noted at the repo include a focus on tasks rather than algorithms; built-in streaming visualisation for data exploration; support for text, images, audio, video, and sensor data; and it can "work with large datasets on a single machine".