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Deep Learning 101 for Dummies like Me

This match had a huge influence on the Go community as AlphaGo invented completely new moves which made people try to understand, reproduce them and created a totally new perspective on how to play the game.

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data inspired by the structure and function of the brain called artificial neural networks.

In a simple case, consider the image on your left, where you have some sets of neurons: The leftmost layer of the network is called the input layer(L1), and the rightmost layer the output layer(L3) (which, in this example, has only one node).

We also say that our example neural network has 3 input units (not counting the bias unit), 3 hidden units, and 1 output unit.

Similarly, if it’s a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.

Loss functions fall under four major category: Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost).

Gradient Decent algorithms can further be improved by tuning important parameters like momentum(which determines the velocity with which learning rate has to be increased as we approach the minima), learning rate(a hyper-parameter that controls how much we are adjusting the weights of our network with respect the loss gradient) etc.

​How to differentiate between AI, machine learning, and deep learning

Sage, a purveyor of business and accounting software, conducted several artificial intelligence (AI) surveys where 43% of respondents in a US survey, and 47% of respondents in a UK survey said that they had no idea about what AI was about.

Very quickly, these technology leaders recognize that they need to put AI and its various subcategories (e.g., machine learning, deep learning) into practice—and into a common business vocabulary that everyone can understand.

The best way to demonstrate these different layers of increasingly complex analytics is by finding a business example that can show the benefits to the decision makers in the business.

For instance, it notices the traffic at certain intersections is most congested in the morning between 6 am and 8 am, or that traffic queues up in the evening, ahead of a sporting event.

SEE: Artificial intelligence: Trends, obstacles, and potential wins (Tech Pro Research) Being able to break down the differences between AI, machine learning, and deep learning is important because it shows management not only the different tiers and capabilities of AI automation but also the increasing levels of business insights that can be gained from it.

In year two, the city will be able to predict traffic jams from rush hour and special event traffic and be able to proactively inform travelers to use alternate routes.

And in year three, the city will be able to develop plans for the future by assessing population (and traffic) growth, infrastructure repair shutdowns and also the impact of factors such as climate change.

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