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Machine Learning Explained

Deep Learning explained: What is it, comparison with ML, applications and requirements

Deep learning is a subset of machine learning (ML) that aims to mimic the human brain without actually matching its ability. It is essentially a neural network with three or more layers, simulating the behaviour of the human brain. While a neural network with a single layer is capable of making approximate predictions, adding hidden layers can optimise and refine the network for accuracy.

A number of artificial intelligence (AI) applications and services are based on deep learning algorithms. They perform analytical and physical tasks without human intervention and thus improve automation. If ML is the most dominant branch of AI then deep learning can be described as the most dominant technology behind everyday products and services.

Deep learning already powers a number of technologies and services such as digital assistants, credit card fraud detection, voice-enabled TV remotes and emerging technologies like robotics, self-driving cars.

How does deep learning differ from machine learning?

As mentioned earlier, deep learning is basically a subset of machine learning, meaning it follows the fundamentals of classical machine learning. The difference stems from the type of data that it works with and the methods used for it to learn.

Machine learning algorithms rely on structured, labelled data to make predictions. In other words, a classical ML model has specific features defined from the input data and organised into tables. While it does not mean that ML models don’t use unstructured data, they do need some kind of pre-processing to organise it into a structured format.

Deep learning eliminates some of this data pre-processing typically involved with machine learning models. The deep learning algorithms are capable of processing unstructured data, including text and images. The deep learning models automate feature extraction, removing some of the dependency on human experts. In ML, the hierarchy of differentiation is manually established by a human expert while deep learning algorithms can determine on their own.

Deep learning algorithm then adjusts and fits itself for accuracy through the processes of gradient descent and backpropagation. This process allows the algorithm to make predictions with increased precision. Machine learning and deep learning are also capable of different types of learning, usually categorised as supervised learning, unsupervised learning, and reinforcement learning.

How does deep learning work?

Deep learning neural networks, also referred to as artificial neural networks, uses a combination of data inputs, weights, and bias to mimic the human brain. All of these elements work together to accurately recognise, classify, and describe objects within the data.

A deep neural network will consist of multiple layers of interconnected nodes. Each of these nodes will build upon the previous layer to refine and optimise the prediction or categorisation. This progression of computations through the network is called forward propagation where the input and output layers are called visible layers. An input layer is where the deep learning model ingests the data for processing and the output layer is where the final prediction or classification is made.

Deep learning neural networks can also use another process called backpropagation to calculate errors in predictions. It will then adjust the weights and biases of the function “by moving backwards through the layers in an effort to train the model”. Both forward propagation and backpropagation allow a neural network to make predictions and even correct for any errors.

A deep learning algorithm relies on different types of neural networks to address specific problems or datasets. The two commonly used neural networks are Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs).

  • Convolutional neural networks: They are primarily used in computer vision and image classification applications. The CNNs are able to detect features and patterns within an image, enabling tasks like object detection or recognition. In 2015, a CNN bested a human in an object recognition challenge for the first time.
  • Recurrent neural network. The RNNs are typically used in natural language and speech recognition applications as it leverages sequential or times series data.

What are the real world use cases?

Deep learning is deeply embedded in our lives and is often integrated in such a way that users are often unaware of the complex data processing enabling the tasks they consider important or simple to use. Here are a few examples of deep learning models that you might encounter every day.

  • Customer Service: A number of organisations now rely on deep learning algorithms to improve their customer service processes. Chatbots used in a variety of applications can be described as a straightforward form of AI. However, a more sophisticated chatbot solution capable of determining whether there are multiple responses to a question, using learning, requires deep learning models. Virtual assistants such as Amazon Alexa, Apple’s Siri or Google Assistant are great examples of deep learning at work.
  • Healthcare: The healthcare industry has seen a wave of digitisation effort where image recognition applications now support medical imaging specialists. These deep learning models help them analyse and assess more images in less time.
  • Financial services: At financial institutions, deep learning models are being used for predictive analytics to drive algorithmic trading of stocks. They are also used to assess business risks for loan approvals, fraud detection, and management of credit and investment portfolios for clients.

Videos on deep learning to watch

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