Tag Archives: deep learning

Wait, machine learning and artificial intelligence aren’t the same thing?

February 5, 2018

Everybody seems to be talking about artificial intelligence and machine learning. Ever since Spielberg’s 2001 movie A.I. Artificial Intelligence, the abbreviation AI has been readily recognizable. News outlets have recently carried headlines such as, “AI in your car can brake faster than you”, or “Police use AI to predict crime”, or similar flashy statements. But… Read More »

Who cares if unsupervised machine learning is supervised learning in disguise?

January 11, 2018

Previously, we saw how unsupervised learning actually has built-in supervision, albeit hidden from the user. In this post we will see how supervised and unsupervised learning algorithms share more in common than the textbooks would suggest. As a matter of fact, both classes can use identical equations for creating mathematical models of the data, and… Read More »

Supervised learning in disguise: the truth about unsupervised learning

December 12, 2017

One of the first lessons you’ll receive in machine learning is that there are two broad categories: supervised and unsupervised learning. Supervised learning is usually explained as the one to which you provide the correct answers, training data, and the machine learns the patterns to apply to new data. Unsupervised learning is (apparently) where the… Read More »

Building Deep Learning Machines: The Hardware Wars Defining the Future of AI

November 6, 2017

So often, conversations regarding deep learning are focused entirely on software and programming — who can train deep learning models to perform X use case better than anyone else? But developments on the hardware side of the equation reveal that there is more to advancing artificial intelligence. As conventional big data analytics emerged, the industry… Read More »

Fast Track Business Outcomes from Artificial Intelligence with Proven Methods and Accelerators

October 23, 2017

Deep learning, chat bots, smart machines, natural language processing, neural networks — these buzzwords continue to create much excitement, hype and urgency among business leaders, as they deliberate on how and when to invest in artificial intelligence (AI) to transform the way their companies can leverage data to improve business outcomes and gain competitive advantage.… Read More »

The New Wave of Machine Learning

October 12, 2017

In the last few years, analytics has evolved significantly. We’ve moved from the adoption of big data technology that was all about Hadoop and Spark, to an increased focus on machine learning algorithms, deep learning and artificial intelligence (AI). In the last year alone, I’ve noted a renewed hype around machine learning, which together with the… Read More »

Survey: State of Artificial Intelligence for Enterprises

October 11, 2017

Artificial intelligence will have a profound impact on our lives and work. Soon it will be everywhere, from our homes to our cars to our offices. Many believe it will cause a massive displacement of jobs. Others argue that it will enhance our decision-making and make us smarter. But, what do business executives at some… Read More »

The Tree of Machine Learning Algorithms

October 10, 2017

The Tree of Machine Learning Algorithms is a simplified schema to rationalize the types of learning paradigms used by categories of algorithms. Just as a tree’s branches grow stronger and wider with an expansive network of roots, the machine learning tree is strengthened using a network of data. Data can be structured or unstructured, involve transactions… Read More »

Could the English language get any more confusing?

September 14, 2017

Did you know that the English language consists of over 200,000 words? Of these 200,000 words, many have the same meaning but different spelling (synonyms). Some words even have the same spelling but different meanings (homographs). Confusing, right? Let’s imagine you were talking to your colleagues about your recent trek to the coast and you… Read More »

Occam’s razor and machine learning

September 12, 2017

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available… Read More »