Deep Learning vs Machine Learning
Even before the advent of deep learning, we’ve been creating smarter processors that can help us do complex tasks like IBM Watson.
But now that machine learning has been divided into subsets, many of these terms get confused or conflated. Take deep learning vs machine learning; one is a part of the other, but often is thrown about interchangeably.
So, today, if you’re interested in fully exploring what deep learning really means; we’ve created this piece on deep learning vs machine learning. And we’ll also cover all the recent advancements in this popular subset to see if it’s this technology you need to consider or if standard machine learning algorithms are all you need to leverage powerful AI.
What is Machine Learning?
Machine learning is the creation and use of computer systems to interpret and make independent conclusions on vast amounts of data – usually looking for patterns. It includes systems that are taught by humans (supervised) and those that are not (unsupervised). Machine learning is a part of artificial intelligence and is chopped down into smaller subsets that include deep learning.
What is Deep Learning?
Deep learning is a kind of artificial neural network that processes data across multiple layers to get more high-level features from it. This computer system tries to simulate the way humans learn by example. This advanced algorithm can process unstructured data like text, images and documents. As such, it’s used for complex tasks like self-driving cars, object identification, voice assistance and more.
Deep Learning vs Machine Learning – What’s the Difference?
First, it’s important to establish that machine learning is the overarching category within artificial intelligence where deep learning resides. That means deep learning is a part of machine learning. Machine learning includes algorithms that are taught by humans (supervised or semi-supervised) using labelled data sets and deep learning does not. With deep learning, the system learns on its own. Just think of Amazon Alexa, every time you let it know of an incorrect response, it’s learning to anticipate on its own and more accurately what you would have wanted to get back instead.
Advancements in Deep Learning
Over the years, since the emergence of deep learning, there have been a number of advancements that are improving this technology.
1. Transfer learning
This approach uses pre-trained models to help get around the need for gigantic datasets. As science has evolved, there are now so many big datasets out there that it’s possible to use information from one problem to help solve another. This is achieved via Markov logic networks and Bayesian networks; first-order logic and probabilistic graphical models.
2. Natural language processing
A subset of AI, NLP helps systems understand human speech so they can solve problems. The emergence of this technology augments deep learning by allowing it to query responses via language and receive voice inputs of new data or requests. This enhances its self-learning function as well as increases deep learning’s scope of use. And it’s an example of how the relationships between deep learning vs machine learning intermingle.
3. Atomistic simulation
This simulation looks at the way biological systems act on the atomic level. According to the Royal Society of Chemistry, “Machine learning potentials (MLPs) for atomistic simulations have an enormous prospective impact on materials [modelling], offering orders of magnitude speedup over density functional theory (DFT) calculations without appreciably sacrificing accuracy in the prediction of material properties.” By putting this data into deep learning environments, we’ll be able to better understand how atoms will react and this could have a huge impact on a number of fields from product formulation to space exploration.”
4. Materials imaging
As machines can now apply their learning techniques to images, this presents an opportunity for the classification and identification of ceramics, polymers, metals and more. With the deployment of this technology within the chemical & material science fields becoming more widespread; the benefit to prediction tasks is likely to improve the quality of assumptions made in these fields.
5. Spectral analysis
Spectral clustering is a new approach to data analysis that lumps data into smaller dimensional clusters. This takes all the unorganised data points and sorts them based on similarities. Overall, it makes handling complex multidimensional datasets easier. The applications for spectral analysis are vast and include chemistry, astrophysics and more. According to DeepAI, “Due to its simple implantation and relatively low performance requirements compared to other clustering techniques, spectral clustering is one of the most popular forms of multivariate statistical analysis.”
What can deep learning or machine learning do for your business?
Machine learning and deep learning can have a positive effect on your business. From predictive analytics for b2b sales to finding the next innovative product feature, the options are endless. When considering the implementation of deep learning vs machine learning, it’s a matter of need. For most businesses, simple supervised ML algorithms will meet most requirements. Deep learning is reserved for the most complex of tasks like self-driving forklifts and smart transport routing systems. General machine learning algorithms are already smart enough to help you capitalise on buying trends, manage churn and forecast expertly.
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Deep Learning vs Machine Learning – Summary
Overall, deep learning is a popular and growing part of machine learning technology. However, for most organisations, deep learning is too advanced for its applications. Standard supervised or semi-supervised algorithms can offer a load of business insights without the need for a multi-million-pound investment. If you’d like to bring your company into the future and see how our machine learning technology can help you make better decisions through predictive analytics for sales; let’s have an initial call. Our team can advise on where you’d get the most benefit from our software and what returns you can expect to see.
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Further Read:
James Wang (2017): How Much Artificial Intelligence Does IBM Watson Have?
Devin Soni (2018): Introduction to Bayesian Networks
Journal of Applied Physics: Image-based machine learning for materials science
World Economic Forum: How deep learning can improve productivity and boost business
