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Machine learning vs deep learning vs neural networks: What’s the difference?

These three subdivisions of AI pose different opportunities for businesses


The terms machine learning and deep learning can seem interchangeable to most people, but they aren’t. Both considered subdivisions within the world of artificial intelligence (AI), the two have many differences, especially in the architecture and use cases.

Machine learning, for instance, uses structured data and algorithms to train models, with the more data at disposal generally equating with more accurate and better trained models. The idea is to eliminate the need for human intervention. Deep learning, on the other hand, is a subset of machine learning and uses neural networks to imitate the way humans think, meaning the systems designed require even less human intervention. 

Differentiating the two, in this way, is crucial to AI research and practical application of both, particularly as businesses attempt to integrate such technologies into their core processes, and recruit for skilled individuals to fill technical roles

What is machine learning?

If you have ever communicated with a chatbot, used predictive text, or watched a show Netflix has recommended for you, then chances are you have used something built on machine learning. Machine learning, which itself is a subset of AI, is a general term used to describe machines learning from data. 

It uses structured data such as numbers, text, images, financial transactions, and so on, as well as algorithms, to replicate the ways humans learn how to do things. Data is gathered and used as training data to guide the machine learning model. The more data that’s used, in theory, the better the model will work. In essence, machine learning is about letting computers learn to programme themselves through use of training datasets and occasional human intervention.

There are different types of machine learning, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. 

Supervised learning involves data scientists giving labelled training data to algorithms to define variables they need for the algorithm to evaluate for connections. Unsupervised learning, on the other hand, involves unlabelled data being processed by algorithms that instinctively hunt for meaningful correlations. Falling between these two methodologies, semi-supervised learning is used to help the model’s own understanding of the dataset. Reinforcement learning, meanwhile, involves a machine completing a sequence of decisions to achieve a goal in an unknown, complex environment.

What is deep learning?

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Deep learning is a subset of machine learning that uses layers of artificial neural networks to imitate how humans think and learn. 

Up until now, it’s been difficult for neural networks to learn much due to the lack of computing power available. This has changed greatly by improvements in big data analytics that allow for larger, sophisticated neural networks that permit machines to perceive, learn, and respond to complex scenarios quicker than humans.

These layers – “deep” neural networks – are constructed to enable data to be transmitted from node to node, like neurons, in a highly connected manner. While huge amounts of data are needed to feed and build such models, they can give us immediate results with relatively little intervention once the model is in place. There are many ways to perform deep learning.

Convolutional Neural Networks (CNNs): These comprise multiple layers and are mostly used for image processing and object detection.

Recurrent Neural Networks (RNNs): These are types of artificial neural network that use sequential data or time series data. They are frequently used in problems, such as language translation, natural language processing (NLP), speech recognition, and image captioning.

Long Short-Term Memory Networks (LSTMs): These are types of Recurrent Neural Network (RNN) that can learn and remember long-term dependencies. They can be useful for complex problem domains like machine translation, speech recognition, and more.

Generative Adversarial Networks (GANs): These are generative deep learning algorithms that produce new data instances that look like the training data. It comprises two parts; a generator, which learns to generate false data, and a discriminator, which learns from that fake information. These networks have been used to produce fake images of people who have never existed as well as new and unique music.

Radial Basis Function Networks (RBFNs): These networks have an input layer, a hidden layer, and an output layer and are typically used for classification, regression, and time-series predictions.

Multilayer Perceptrons (MLPs): These are a type of feedforward (this means information moves only forward in the network) neural networks. These have an input layer and an output layer that are fully connected. There may also be hidden layers. These are used in speech-recognition, image-recognition, and machine-translation software.

Deep Belief Networks (DBNs): This looks like another feedforward neural network with hidden layers, but isn’t. These are a sequence of restricted boltzmann machines which are sequentially connected. These are used to identify, gather and generate images, video sequences and motion-capture data.

What are the major differences between machine learning and deep learning?

While the two terms often get confused with each other, deep learning is a subset of machine learning. However, deep learning differentiates itself from machine learning by the data types it works with and the methods by which it learns. 

Machine learning uses structured, labelled data to predict outcomes. This means a machine learning model’s input data defines specific features and is organised into tables. While it gets progressively better at carrying out the task in hand, there still requires there to be a human to intervene at points to ensure the model is working in the required way. In other words, if the predictions are not accurate, an engineer will make any adjustments needed to get back on track.

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On the other hand, deep learning models have algorithms that can figure out if their predictions are accurate using its neural network without human involvement.

Another difference is that where machine learning can use small amounts of data to make predictions, deep learning needs much, much more data to make more accurate predictions.

While machine learning needs little time to train – typically a few seconds to a few hours –  deep learning takes far longer as the algorithms used here involve many layers.

Outputs also differ between the two. Machine learning tends to output numerical values, such as a score or classification, while deep learning can output in multiple formats, such as text, scores, or even sounds and images.

What are the different uses and applications of machine learning vs deep learning?

Machine learning is already in use in a variety of areas that are considered part of day-to-day life, including on social media, on email platforms and, as mentioned, on streaming services like Netflix. These types of applications lend themselves well to machine learning, because they’re relatively simple and don’t require vast amounts of computational power to process complicated decision-making.

Among some of the more complex uses of machine learning are computer vision, such as facial recognition, where technology can be used to recognise people in crowded areas. Handwriting recognition, too, can be used to identify an individual from documents that  are scanned en masse. This would apply, for example, to academic examinations, police records, and so on. Speech recognition, meanwhile, such as those used in voice assistants are another application of machine learning.

Because of the nature of deep learning, on the other hand, this technology allows for far more complex decision-making, and near-fully autonomous systems, including robotics and autonomous vehicles

Deep learning also has its uses in image recognition, where massive amounts of  data is ingested and used to help the model tag, index, and annotate images. Such models are currently in use for generating art, in systems like DALL·E. Similarly to machine learning, deep learning can be used in virtual assistants, in chat bots, and even in image colorisation. Deep learning has also had a particularly exciting impact in the field of medicine, such as in the development of personalised medicines created for somebody’s unique genome.

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