Enter machine learning and deep learning, and you have the siblings waiting to reveal their contrasting characteristics. First and foremost, machine learning and deep learning come under the Artificial Intelligence umbrella. The two can walk hand in hand, where deep networks can be used in an ensemble with ML algorithms, or work independently to solve several business problems. And for a deep learning vs machine learning dive, the prerequisite hinges on the ‘learning’ concept that separates them.
Taught vs self-taught
A student learning classical music relies on his teacher feeding him with the nous and other features of varied forms, which may take the form of musical notes and rhythmical patterns. The student grasps the musical essence in terms of notes and rhythms to identify musical tones that are similar or ones that carry the same musical aura.
Take another individual who can spot musical birds with the same feather, so to speak, in being a self-taught learner. Listening to different compositions and relying on the neurons to make the connections, he is able to tell apart tunes that have similar characteristics.
Taught and the self-taught concepts set Machine learning and Deep Learning apart.
Deep Learning vs Machine Learning Divide
What are the areas of focus to distinguish deep learning from machine learning?
In machine learning, you feed features into a model. Take predictive maintenance for instance, wherein there is a need for feature selection and feature expansion.
Deep learning, on the other hand, picks features from the data to distinguish, identify and predict. In using deep learning for predictive maintenance, sensor parameters will make up for the data feed into deep neural networks and the deep learning models will enable the end-to-end prediction, executed by way of constructing health and other factors and predicting the health condition of a machine.
Data Level & Type
Machine learning works when the data level is low or medium, and for using deep learning, the requisite is to have high data levels. For instance, if there are only 50 data points, machine learning algorithms like decision trees would serve the purpose well. And in case of deep learning, it is the labelled data that goes well with the model.
Time it takes to train machine learning is less when compared to deep learning. And to be self-taught, deep learning can consume days or even weeks to acquire that capability
While machine learning is embraced, why a result in terms of prediction, forecasting or classification is thrown as output can be gauged, for we know the factors and features that are being considered to build a model. Deep learning becomes a black box wherein the results offered cannot be interpreted. Let’s look deep into the deep neural network’s performance in predictive maintenance problem. Though results are quite good, determining what neurons are called into play for modelling purpose is beyond human grasp.
Machine Learning vs Deep Learning Validation
In validating the use of machine learning and deep learning in specific business scenarios, what transpires are the factors that matter most when it comes to selecting the best-fit model between the two. Let’s look into sentiment analysis and fraud detection, and how ML and DL can be used in these areas.
Take Sentiment Analysis for instance. While machine learning application becomes the primary force enabling a solution, data from social media, emails, review sites are used to gain textual cues which in turn help read emotions. When the need to scan through videos and photographs arise, for getting a real-time cue of human emotions, deep learning fits in well to augment sentiment analysis.
Credit card fraud detection
When the focus is on historical data around credit card transactions, which would offer cues to detect fraud in the future, machine learning is used for fraud detection. Fraudsters come out with new tricks every now and then to deceive the system and known features like doubled transaction, unusual transaction time and unusual region may just fall short of providing cues to detect fraud. With deep learning application, there is the possibility of extracting complex patterns from unsupervised data in huge amounts and enabling real-time fraud detection.
As machine learning exudes confidence in terms of solving business problems, more and more businesses are building ML models to find answers to specific problems. While deep learning has set the precedence in using loads of data and learning on its own, it is on course to solve some complex and critical problems that could paralyze businesses.