The Connect Between Deep Learning and AI

Deep learning is a sub-field of machine learning and is related to algorithms. Machine learning is a kind of artificial intelligence that provides computers with the ability to learn, without explicitly programming them.

Deep learning is a new area of machine learning research, which has been introduced with the objective of moving machine learning closer to one of its original goals—artificial intelligence (AI). Deep learning is the sub-field of machine learning that is concerned with algorithms. Its structure and function is inspired by that part of the human brain called neural networks. It is the work of well-known researchers like Andrew Ng, Geoff Hinton, Yann LeCun, Yoshua Bengio and Andrej Karpathy which has brought deep learning into the spotlight. If you follow the latest tech news, you may have even heard about how important deep learning has become among big companies such as:

Google buying DeepMind for US$ 400 million

Apple and its self-driving car

NVIDIA and its GPUs

Toyota’s billion dollar AI research investments

All of this tells us that deep learning is really gaining in importance.

Neural networks

The first thing you need to know is that deep learning is about neural networks. The structure of a neural network is like any other kind of network; there is an interconnected Web of nodes, which are called neurons, and there are edges that join them together. A neural network’s main function is to receive a set of inputs, perform progressively complex calculations, and then use the output to solve a problem. This series of events, starting from the input, where each activation is sent to the next layer and then the next, all the way to the output, is known as forward propagation, or forward prop.

The first neural nets were born out of the need to address the inaccuracy of an early classifier, the perceptron. It was shown that by using a layered web of perceptrons, the accuracy of predictions could be improved. This new breed of neural nets was called a multi-layer perceptron or MLP.

You may have guessed that the prediction accuracy of a neural net depends on its weights and biases. We want the accuracy to be high, i.e., we want the neural net to predict a value that is as close to the actual output as possible, every single time. The process of improving a neural net’s accuracy is called training, just like with other machine learning methods. Here’s that forward prop again – to train the net, the output from forward prop is compared to the output that is known to be correct, and the cost is the difference of the two. The point of training is to make that cost as small as possible, across millions of training examples. Once trained well, a neural net has the potential to make accurate predictions each time. This is a neural net in a nutshell (refer to Figure 1).

Three reasons to consider deep learning

When the patterns get really complex, neural nets start to outperform all of their competition. Neural nets truly have the potential to revolutionise the field of artificial intelligence. We all know that computers are very good with repetitive calculations and detailed instructions, but they’ve historically been bad at recognising patterns. Thanks to deep learning, this is all about to change. If you only need to analyse simple patterns, a basic classification tool like an SVM or logistic regression is typically good enough. But when your data has tens of different inputs or more, neural nets start to win out over the other methods.