Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data. It is popular in machine learning and artificial intelligence text books to first consider the learning styles that an algorithm can adopt. There are only a few main learning styles or learning models that an algorithm can have and we'll go through them here with a few examples of algorithms and problem types that they suit. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result. When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods.

Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems. This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

The aim of this workshop is to bring to bear on the expertise of a diverse set of researchers to progress in the evaluation of general purpose AI systems. Up to now, most AI systems are tested on specific tasks. However, to be considered truly intelligent, a system should exhibit enough flexibility to be able to learn how to perform a wide variety of tasks, some of which may not be known until after the system is deployed. This workshop will examine formalisations, methodologies and test benches for evaluating the numerous aspects of this type of general AI systems. More specifically, we are interested in theoretical or experimental research focused on the development of concepts, tools and clear metrics to characterise and measure the intelligence, and other cognitive abilities, of general AI agents.

Think of artificial intelligence, and your brain might recall Will Smith's misadventures in I, Robot. But in reality, AI-powered apps are going to prove themselves useful--even indispensable--in the near-future. The technology at the heart of AI is machine learning--the ability for programs to teach themselves new tricks. The Complete Machine Learning Bundle is aimed at aspiring developers who want to get ahead of the curve. With 10 courses and 63.5 hours of video tutorials, it offers an impressive lineup of content.

Are you familiar with self-driving cars? These things would not be possible without the help of Machine Learning--the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.

In the past few days, I've taught a machine learning algorithm how to write in the style of Harry Potter, Hamilton (the musical), and HBO's Silicon Valley. The mostly non-sensical, occasionally human-like, topically-flavored writing seems to be amusing not only to me, but to many others. Thus, I've made this quick tutorial to teach you how to create your own instances of "Deep Writing". This is not going to be an in-depth description of the underlying technology -- but instead, a step-by-step guide that anybody can follow (even if you have no coding or machine learning experience). Here is a very crude approximation of what is involved in the Deep Writing process. More than anything, this is meant to give you enough intuition and appreciation to follow along with the rest of the tutorial.

H2O is Google-scale open source machine learning engine for R & Big Data. This live webinar introduces Distributed Deep Learning concepts, implementation and results from recent developments. Real world classification & regression use cases from eBay text dataset, MNIST handwritten digits and Cancer datasets will present the power of this game changing technology.

The purpose of this blog post is to demonstrate how to install the Keras library for deep learning. Let me start by saying that Keras is my favorite deep learning Python library. It's a minimalist, modular neural network library that can use either Theano or TensorFlow as a backend. Furthermore, the primary motivation behind Keras really resonates with me: you should be able to experiment super quickly -- going from idea to result, as fast as possible. Coming from a world that mixes both academia and entrepreneurship, the ability to iterate quickly is extremely valuable, especially in the deep learning world where it can take days to weeks to train just a single model.

Coding will join this list in time, however, where it differs wildly from the afore mentioned examples is it is unlikely to be lovingly preserved for future generations to admire, fiddle with or better still, reactivate. Its essence will not be reified for one specific reason – it can't be touched and humans value tactility. We touch immediately, both inside and outside the womb. Today, we find ourselves at a pivotal moment in our existence and about to experience an exponential period of rapid technological growth the likes of which is quite probably beyond our comprehension and at a base level, will have serious implications for coding. We rather arrogantly think that because we have a good grasp of our own technological advancement so far, we can somehow predict the mass cultural and behavioural shift about to happen as we question our own skills in the world. Us techies hold on to the notion that we are the masters of code, and we will be forever commanding line by line, the computers to do our bidding.