Nowadays, when mentioning artificial intelligence, one immediately recalls machine learning or deep learning. Both subfields belong to AI and are indispensable elements of building a learning or decision-making system. At any rate, these two words cannot be said to be similar to each other.
Machine learning is the aspect of artificial intelligence related to developing algorithms to enable computers to learn on fed data and thereafter predict and otherwise make decisions. The final objective is to one day have capable computers that can learn on their own without any supervision from mankind. ML algorithms are data-driven, and their precision increases with huge datasets. In Machine Learning, features are manually selected by experts on which the algorithm is supposed to learn and predict. The ML algorithms include different forms: supervised learning, unsupervised learning, and reinforcement learning, each with its own specific applications and benefits.
Deep Learning (DL) is a sub-field of machine learning that specifically embraces the use of neural networks having multiple layers — hence the name, “deep”. This is due to its inherent ability to handle enormous volumes of unstructured data. The models that have come up are the deep learning models, developed using artificial neural networks that imitate both the structure and function of the human brain. One beauty about deep learning is that the algorithms do automatic feature extraction on their own, without prior manual feature engineering. However, deep learning requires significant computational resources, including GPUs, due to the complexity of the models and the vast amount of data they process.
When comparing machine learning and deep learning, several distinctions become apparent. Machine Learning needs less data and can effectively deal with small datasets, but it works much better when more data are available. On the other hand, deep learning needs large volumes of providing data to work well and accurately. Deep learning excels as the volume of data increases. Machine learning, in contrast, relies upon domain experts and has a very fixed definition of features manually done, which is extremely time-consuming and requires deep knowledge. In turn, deep learning enables automatic data-driven feature extraction, which processes massive and complicated data effortlessly without human intervention. In general, machine learning requires less computational power, and algorithms can run on standard CPUs. Deep learning, on the contrary, demands high computational power, for the cutting-edge model. Each model is so complex that it may lead to hidden layers of information in the model.
Machine learning models are generally easier to interpret. The decision-making process of such models is quite transparent. Deep models, in contrast, are sometimes referred to as black boxes because defining how they do something, on the basis of a decision, can be quite difficult. Here, the training time of deep and machine learning easily varies with machine learning. Machine learning algorithms are usually faster to train since they use a simpler algorithm to deep learning and therefore have fewer parameters. On the other hand, training deep learning models can consume time. This is because the neural networks applied in the process tend to be complex and involve many data manipulations.
Machine learning and deep learning also offer a different application to the real world. Machine learning can be used in spam detection wherein the past data, it will classify the email as spam or not. It is also used in predictive maintenance to forecast equipment failure before it occurs, thereby saving on costs and downtime. Among the other applications are those in fraud detection, in which the identification of fraudulent transactions results from the application of analytics in pattern and anomaly analysis. Applications in deep learning include image and speech recognition, by which models can correctly identify and understand images and speech with high accuracy for use in facial recognition and voice-activated assistants. It enhances the ability for natural language processing (NLP) so the machine is able to understand and react/respond to human language—this is what gives rise to chatbots and translation services. An application area for deep learning is, therefore, autonomous vehicles.
The distinction between machine learning and deep learning needs to be very clear to actually work with them. While machine learning is designed for tasks with less complexity in structured data and less computational power, deep learning, with the ability to handle huge amounts of unstructured data, is tasked at complex, data-intensive tasks.