AI News, An Introduction to Deep Learning

An Introduction to Deep Learning

Deep Learning is at the cutting edge of what machines can do, and developers and business leaders absolutely need to understand what it is and how it works.

Nielsen, “Neural Networks and Deep Learning” The great reveal about Neural Nets (and most Machine Learning algorithms, actually) is that they aren’t all that smart – they’re basically just feeling around, through trial and error, to try and find the relationships in your data.

In his popular Coursera course on Machine Learning, Professor Andrew Ng uses the analogy of a lazy hiker to explain how most algorithms end up working: “we place an imaginary hiker at different points with just one instruction: Walk only downhill until you can’t walk down anymore.” (Slate) The hiker doesn’t actually know where she’s going – she just feels around to find a path that might take her down the mountain.

Machine Learning has been used for classification on images and text for decades, but it struggled to cross the threshold – there’s a baseline accuracy that algorithms need to have to work in business settings.

It’s not just a marginal improvement, but a game changer: “Asked whether two unfamiliar photos of faces show the same person, a human being will get it right 97.53 percent of the time.

New software developed by researchers at Facebook can score 97.25 percent on the same challenge, regardless of variations in lighting or whether the person in the picture is directly facing the camera.” Speech recognition is a another area that has felt Deep Learning’s impact.

Baidu – one of the leading search engines of China – has developed a voice recognition system that is faster and more accurate than humans at producing text on a mobile phone;

We’re just at the cusp of developers and business leaders understanding how they can use Machine Learning to drive business outcomes, and having more available tasks at your fingertips because of Deep Learning is going to transform the economy for decades to come.

The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.” Caffe – “Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms.

You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries.” Torch – “Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.

It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.” Theano (used by many of the above) – “Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).

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