What is deep learning?

What do digital assistants, self-driving cars, and credit card fraud detection have in common?

The answer: deep learning technology. It’s the technological innovation behind many of our most common products and services, and an industry within data science that is skyrocketing with revenue from deep learning activity expected to exceed $18.3 billion in 2024. The number of enterprises relying on deep learning for digital transformation initiatives is also set to increase exponentially by 2030.

So, what is deep learning – and how is it used? What are the advantages to investing in deep learning techniques? And what might the future of deep learning have in store?

What is deep learning?

Amazon defines deep learning (DL) as “a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain”. In fact, DL rose to the forefront of public consciousness when a robot defeated the world champion in a game of AlphaGo – hailed as the world’s most complex board game – in 2016.

DL models can recognise and interpret complex patterns within text, images, audio, and other types of data and use these insights to make highly accurate predictions. DL drives numerous artificial intelligence (AI) services and applications that improve automation and optimisation – it can perform any number of physical and analytical tasks without a need for human intervention. For example, transcribing audio files into text, and making suggestions of user preferences based on historical data.

How does deep learning work?

Deep learning algorithms continually analyse data with a given, logical structure. To do this, the technology uses multi-layered structures of algorithms known as deep learning neural networks – modelled on the neural networks and neurons that exist in the human brain. Networks contain two hyperparameters that control their architecture and topology: the number of layers, and the number of nodes per hidden layer.

These artificial neural networks allow us to perform certain data-based tasks, such as classification, regression, and clustering. For example, they can group and sort unlabelled data according to similarities among samples, or be trained to group and classify samples of labelled data into various categories.

There are many different types of algorithms used in deep learning, such as:

• recurrent neural networks (RNNs)
• convolutional neural networks (CNNs)
• self organising maps (SOMs)
• long short term memory networks (LSTMs)
• multilayer perceptrons (MLPs)

Depending on how the specific neural network is used, the DL learning process may use supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning or reinforcement learning.

What is the difference between machine learning and deep learning?

Deep learning is a subset of machine learning (ML), which itself is a subset of AI. Many traditional machine learning algorithms and machine learning models have a finite capacity to learn – regardless of how much data they can acquire. In contrast, deep learning systems only improve their experience as data volume increases.

What are some examples of deep learning in practice?

Deep learning technologies have transformed our personal and professional lives, finding prominence in just about every business sector. It’s widely used in industries such as manufacturing, e-commerce, entertainment, insurance, advertising and healthcare.

Some examples of deep learning applications include:

• facial and image recognition
• generative AI
• robotics
• chatbots
• generating relevant advertisements, as used by social media platforms
• predictive analytics
• personal assistants, such as Alexa and Siri
• speech recognition – also known as natural language processing (NLP)
• object detection

Let’s look at some DL use cases in different industries.

Healthcare: DL supports disease detection and computer-aided diagnosis. It has applications across drug discovery, medical imaging, medical research, and the diagnosis of life-threatening conditions.

Financial services: DL is integral to fraud prevention and detection. Predictive analytics technology, as used by PayPal, detects and protects against fraudulent activity by examining patterns of user behaviour to increase anomaly detection.

Entertainment: DL provides highly personalised recommendations – across any number of streaming platforms. Its technologies analyse consumer interests, behaviours and browsing histories to make further content suggestions.

What are the advantages and disadvantages of deep learning?

Advantages of deep learning include:

• high-performance learning systems – in terms of speed, efficiency and scalability – when analysing huge volumes of data
• the same deep neural network approach can be applied to various types of application and datasets
• lack of feature extraction
• deep learning network architecture is highly flexible, enabling it to be adapted to future problems and use cases
• allows for massive parallel computations to be performed.

Disadvantages of deep learning include:

• it requires huge amounts of input data if it is to outperform other techniques
• high computational requirements and expenses associated with the training process, graphics processing units (GPUs), storage and other types of software and hardware
• overlifted outcomes, where models perform well with training data but badly with unseen data
• interpretability issues related to how a model operates or arrives at decisions
• ethical and legal considerations, such as models amplifying biases and using private data and intellectual properties.

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