A Tour of Machine Learning Algorithms In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and categorize the algorithms you may come across in the field. The first is a grouping of algorithms by the learning style.The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together). Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types. After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related. Algorithms Grouped by Learning Style 1. 2. 3.
R for Data Science How to get started with Machine Learning on Bluemix There is a lot of talk about artificial intelligence (AI) these days, especially since Google’s AlphaGo beat a Go world champion. Companies like IBM are using this technology already in a number of products. For example on Bluemix developers can easily consume cognitive Watson services like speech or image recognition that use machine and deep learning under the cover. Since this technology looks so promising and powerful I’m trying to learn machine learning. However I found the open source framework Scikit Learn which seems powerful and at the same time it provides relative simple samples to get started. You can run this sample easily via Bluemix. One nice thing about Scikit Learn is that it provides some sample data for your first steps in machine learning. The core difference to classic programming is that you don’t code any longer rules. The dataset is divided in two parts. In order to run this sample on Bluemix, you can use a Docker image from my colleague Peter Parente.
Start Here With Machine Learning Get Started and Get Good at Applied Machine Learning Hi, Jason here. I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. I teach a top-down and results-first approach designed for developers and engineers. Access my best free tutorials on the blog or take the next step with my paid training material. You may be feeling overwhelmed. Take your time. Table of Contents What do you need help with? How Do I Get Started? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: For more on this top-down approach, see: Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists. Applied Machine Learning Process The benefit of machine learning are the predictions and the models that make predictions. Machine Learning Algorithms Deep Learning
A Neural Network Playground Um, What Is a Neural Network? It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. What Do All the Colors Mean? Orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values. The data points (represented by small circles) are initially colored orange or blue, which correspond to positive one and negative one. In the hidden layers, the lines are colored by the weights of the connections between neurons. In the output layer, the dots are colored orange or blue depending on their original values. What Library Are You Using? We wrote a tiny neural network library that meets the demands of this educational visualization. Credits This was created by Daniel Smilkov and Shan Carter.
ConvNetJS: Deep Learning in your browser ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2) cost functions Ability to specify and train Convolutional Networks that process images An experimental Reinforcement Learning module, based on Deep Q Learning. Head over to Getting Started for a tutorial that lets you get up and running quickly, and discuss Documentation for all specifics. Code The code is available on Github under MIT license and I warmly welcome pull requests for new features / layers / demos and miscellaneous improvements. Discussion Group
Teorías, hechos y mentes | Fronteras Estamos viviendo un momento clave en el desarrollo económico de nuestras sociedades, y tal vez en la historia misma de la humanidad, como es la creación de verdaderos sistemas de Inteligencia Artificial. El avance del Big Data, el desarrollo y el éxito de técnicas como el Deep Learning y los ejemplos anecdóticos que empiezan a aparecer ya en nuestras vidas son sólo premoniciones de lo que se viene: un verdadero tsunami económico y social que va a suponer una seria convulsión política. Los espectaculares avances como AlphaGo ganando al Go a las mejores mentes humanas o programas relativamente simples capaces de colorizar imágenes en blanco y negro o diagnosticar enfermedades pronto darán paso a chatbots capaces de reemplazar a los call centers y programas de gestión que hagan innecesarios a ejecutivos intermedios o abogados. Y es cierto que como humanos las características de nuestro cuerpo y nuestra mente encauzan y limitan nuestras posibilidades de percibir el universo.
10 Machine Learning Examples in JavaScript Machine learning libraries are becoming faster and more accessible with each passing year, showing no signs of slowing down. While traditionally Python has been the go-to language for machine learning, nowadays neural networks can run in any language, including JavaScript! The web ecosystem has made a lot of progress in recent times and although JavaScript and Node.js are still less performant than Python and Java, they are now powerful enough to handle many machine learning problems. Web languages also have the advantage of being super accessible - all you need to run a JavaScript ML project is your web browser. Most JavaScript machine learning libraries are fairly new and still in development, but they do exist and are ready for you to try them. 1. Brain is a library that lets you easily create neural networks and then train them based on input/output data. Deep playground Educational web app that lets you play around with neural networks and explore their different components. Synaptic
Machine Learning Repository Neural Networks: How they work, and how to train them in R With the current focus on deep learning, neural networks are all the rage again. (Neural networks have been described for more than 60 years, but it wasn't until the the power of modern computing systems became available that they have been successfully applied to tasks like image recognition.) Neural networks are the fundamental predictive engine in deep learning systems, but it can be difficult to understand exactly what they do. To help with that, Brandon Rohrer has created this from-the-basics guide to how neural networks work: In R, you can train a simple neural network with just a single hidden layer with the nnet package, which comes pre-installed with every R distribution. Data Science and Robots Blog: How neural networks work
Introduction to Strategic Thinking Starts June 22, 2015 This is a short interdisciplinary course on strategic thinking and some of its most powerful tools. Strategic thinking is not exclusive to business or military applications. The skills taught in this course can be used by everyone. Young professionals can use the knowledge to effectively plan their careers, stay-at-home mothers can use it to improve how they communicate with their children, and entrepreneurs can use it to better position their business in the marketplace. We will draw lessons and use concepts from economics, game theory, scenario planning, behavioral sciences, and futures studies, as well as philosophy and linguistics. This course is designed to be a starting point for people who are just beginning to learn about strategic thinking. "Fascinating course. "The course was very enlightening! "Thank you for the stimulus and providing a completely different approach from what I was expecting from this class." Sandjar Kozubaev Economist & Strategist
Data Types 101 Ever looked at your data and wondered how and where to get started? If you don't know the difference between quantitative data and qualitative data then you're in the right place. Here is our guide to data types and how to deal with them... Data Types Ultimately, there are just 2 data types. Surely they are all just fancy words made up by mathematicians and statisticians to make them sound important, aren't they? Well actually, they are pretty important, because if you know what types of data you have, then you know what maths and stats operations you're allowed to use on your data Get that wrong and you're skating on pretty thin ice - sooner or later you're going to make your boss rather unhappy, and nobody wants that, do they? So take a deep breath and let's go I promise this will all be quite painless... The Difference Between Quantitative Data and Qualitative Data So to put it in simple terms: Quantitative data is measured Qualitative data is categorised Nominal Data Ordinal Data Interval Data