What is deep learning?

Deep learning is a rapidly expanding area of artificial intelligence (AI) that powers some of technology’s most well-used and ambitious applications, from Apple’s Siri and Amazon’s Alexa to self-driving cars.

As technology has advanced – including significant improvements to computing power, algorithms, graphics processing units (GPUs), and so on – and big data has become more integral across numerous sectors, demand for deep learning has grown.

But what exactly is deep learning, and how does it work?

Deep learning is considered a subset of machine learning, one that uses artificial neural networks that have been developed to mimic the neural pathways of the human brain and function in much the same way. Through these neural networks and deep learning algorithms, machines can:

• Learn.
• Make accurate predictions.
• Solve complex problems.

Deep learning models are trained by analysing large datasets, and their network architectures include multiple layers for processing and extracting increasingly complex information from input data.

There are several kinds of these multi-layer architectures – which typically include an input layer, a hidden layer, and an output layer – but a few examples of neural network models are:

• Recurrent neural networks (RNNs), which can process connections forwards and backwards.
• Convolutional neural networks (CNNs), which are often used for image recognition, video analysis, and natural language processing tasks.

It’s also worth noting that deep learning computers continue to learn as they operate, so the more data a deep learning computer analyses, the better it will perform.

How is deep learning used?

Deep learning systems are used for a diverse range of artificial intelligence applications, and across a number of different industries. Use cases include:

Image recognition

Deep learning algorithms can be used for image classification and computer vision applications, such as facial recognition software, and can identify objects in even the smallest of pixels.

Speech recognition

Speech recognition is one of the most commonly used deep learning applications, supporting virtual assistants such as Siri, Alexa, and Microsoft’s Cortana. It’s also used for applications that transcribe speech to text.

Natural language processing

Natural language processing works to analyse and understand human language. It’s commonly used for language translation apps, sentiment analysis work, social media, and online chatbots.

Predictive analytics

Deep learning plays a significant role in data science, assisting data scientists with data analysis as well as statistical and predictive modelling. 

Robotics

Robotics uses deep learning to support machines as they learn spatial awareness and perform tasks such as grasping objectives. Through deep learning, robotic machines can better process their sensor inputs and improve their accuracy in different scenarios and situations.

Gaming

Video games harness deep learning in a number of ways. For example, in-game characters powered by artificial intelligence can learn from experiences – which enhances the user experience – and deep learning can even be used for game content creation.

Finance

Deep learning works to power and optimise financial software used to predict stock prices, detect fraud, and analyse financial data.

Healthcare

One of the most game-changing areas of deep learning is in healthcare, with deep learning technology being used to help diagnose diseases through medical images and observations, predict patient outcomes, and assist with medical imaging.

Autonomous vehicles

Through deep learning models, self-driving cars can recognise and respond to traffic lights, pedestrians, and other obstacles on the road.

Deep learning software and applications

The number of software platforms and apps that harness deep learning technology is an ever-growing figure, but a few popular examples include:

• TensorFlow. Developed by Google, TensorFlow is an open-source platform for building and training machine learning and deep learning models.
• PyTorch. Developed by Meta AI and now part of the Linux Foundation, PyTorch is another popular open-source framework for building and training deep learning models.
• Keras. Keras is an open-source deep learning library written in Python. It is designed to enable fast experimentation with deep neural networks and is widely used for image classification and natural language processing.
• OpenCV. OpenCV is an open-source computer vision library with deep learning capabilities. It can be used for tasks such as object detection, facial recognition, and image segmentation.

What are the benefits and drawbacks of deep learning?

Deep learning methods offer several benefits and are widely used across many industries. For example, deep learning can be used to:

• Handle complex data. Deep learning algorithms are particularly good at handling large amounts of complex data – both structured data and unstructured data – including images, video, and text. Within this data, deep learning algorithms can learn to recognise patterns and features that may be difficult, or even impossible, for humans to identify, all in real-time.
• Improve accuracy. Deep learning algorithms can achieve higher levels of accuracy than traditional machine learning algorithms.
• Automate feature engineering. With traditional machine learning, engineers need to manually identify and extract relevant features from the data. With deep learning, the algorithms can automatically learn the features from the data, reducing the need for manual feature extraction and engineering.

However, deep learning is not without its limitations. For example, it requires:

• Large amounts of data. Deep learning algorithms require large amounts of training set data to be effective. Without enough new data, the algorithms may perform poorly.
• High computational power. Training deep learning models can be computationally intensive, requiring large amounts of computing power and time. This can make deep learning less accessible to small businesses and people without access to powerful computing resources.
• Good-quality data. If deep learning algorithms learn from biased or flawed data, it can lead to biases or flaws in predictions. This is why it’s important to ensure that the training data used to teach deep learning models is representative and of high quality.

What is the difference between deep learning and machine learning?

Machine learning and deep learning are both subsets of artificial intelligence, but they differ in their learning techniques.

Machine learning models use algorithms to automatically learn patterns in data and make predictions or decisions. These algorithms can be divided into two main types:

• Supervised learning, which uses labeled data to train the algorithm.
• Unsupervised learning, which uses unlabeled data to train the algorithm.

Deep learning, on the other hand, is a subfield of machine learning that uses artificial neural networks to learn from data, and is particularly well-suited for handling complex data.

So while both machine learning and deep learning use algorithms to learn from data, deep learning is a more specialised subset of machine learning, and is unique in that it uses artificial neural networks to automatically learn multiple layers of representations from the data.

Develop expertise in deep learning and AI

Explore deep learning in greater depth while gaining specialist skills in a fast-growing area of computer science with the MSc Computer Science with Artificial Intelligence at Abertay University. This flexible master’s degree is taught part-time and 100% online, and is suited to mid-level professionals who want to move into computer science, or take the next step in their existing computer science career.

You will develop specialist knowledge in artificial intelligence, including how to use it to enhance processes and how businesses and systems can be improved through AI techniques. One of your key modules in artificial intelligence will enable you to apply AI directly to problems, which will involve labelling, building, training, and deploying a custom machine learning model. 

You will also gain a firm foundation in the field of computer science, with core knowledge in areas such as networking, databases, and web development.