What is deep learning? Everything you need to know
Deep learning is a subset of machine learning, which itself falls within the field of artificial intelligence.
Artificial intelligence is the study of how to build machines capable of carrying out tasks that would typically require human intelligence.
That rather loose definition means that AI encompasses many fields of research, from genetic algorithms to expert systems, and provides scope for arguments over what constitutes AI.
Within the field of AI research, machine learning has enjoyed remarkable success in recent years — allowing computers to surpass or come close to matching human performance in areas ranging from facial recognition to speech and language recognition.
Machine learning is the process of teaching a computer to carry out a task, rather than programming it how to carry that task out step by step.
At the end of training, a machine-learning system will be able to make accurate predictions when given data.
That may sound dry, but those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, if a person is crossing in front of a self-driving car, whether the use of the word bookin a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video.
Machine learning is typically split into supervised learning, where the computer learns by example from labeled data, and unsupervised learning, where the computer groups similar data and pinpoints anomalies.
Deep learning is a subset of machine learning, whose capabilities differ in several key respects from traditional shallow machine learning, allowing computers to solve a host of complex problems that couldn’t otherwise be tackled.
An example of a simple, shallow machine-learning task might be predicting how ice-cream sales will vary based on outdoor temperature. Making predictions using only a couple of data features in this way is relatively straightforward, and can be carried out using a shallow machine-learning technique called linear regression with gradient descent.
The issue is that swathes of problems in the real world aren’t a good fit for such simple models. An example of one of these complex real-world problems is recognizing handwritten numbers.
To solve this problem, the computer needs to be able to cope with huge variety in how the data can be presented. Every digit between 0 and 9 can be written in myriad ways: the size and exact shape of each handwritten digit can be very different depending on who’s writing and in what circumstance.
Coping with the variability of these features, and the even bigger mess of interactions between them, is where deep learning and deep neural networks become useful.
Neural networks are mathematical models whose structure is loosely inspired by that of the brain.
Each neuron within a neural network is a mathematical function that takes in data via an input, transforms that data into a more amenable form, and then spits it out via an output. You can think of neurons in a neural network as being arranged in layers, as shown below.
All neural networks have an input layer, where the initial data is fed in, and an output layer, that generates the final prediction. But in a deep neural network, there will be multiple “hidden layers” of neurons between these input and output layers, each feeding data into each other. Hence the term “deep” in “deep learning” and “deep neural networks”, it is a reference to the large number of hidden layers — typically greater than three — at the heart of these neural networks.
This simplified diagram above hopefully helps to provide an idea of how a simple neural network is structured.
