Machine learning and deep learning are the two main viewpoints within the data science field and sub-sections of the wider area of artificial intelligence. Newcomers to machine learning often interchange the notion with deep learning and AI - believing they are the same. Although they are related, these three terms have distinct meanings.
Artificial intelligence, deep learning, and machine learning are popular terms in enterprise IT and are occasionally deemed interchangeable, particularly so when enterprises market their digital products. The terms, however, are not substitutable, and they all have important distinctions.
Artificial intelligence refers to the digital cloning of human intelligence using computerized devices. Its definition is continually evolving, in concert with new technologies. As advanced simulation techniques evolve to simulate improved human thinking, so do the capabilities and limitations of AI are re-evaluated.
Diagrammatically – How DL, ML and AI can be viewed
And these maturing simulation technologies comprise machine learning. It is important to note that deep learning is a sub-division of machine learning, and neural networks and deep learning overlap widely (there are a few deep learning techniques not using neural networks, and small neural networks do not qualify for deep learning).
What exactly is Machine Learning?
Machine learning is broadly defined as the ability of a computerized machine algorithm to emulate intelligent human behavior by learning on data. Artificial intelligence systems have the same goal, but how they can achieve it is not specified.
We all come across a wide variety of machine learning applications in our daily lives, such as interacting with chatbots, using language translation web applications, and comprehending the movies that streaming operators have suggested that we watch. ML even governs the presentation of our social media feeds. Machine learning powers autonomous guided vehicles and drives those complex machines that can diagnose medical conditions based on X-ray imagery.
The primary goal of AI is to design and develop computer models that demonstrate intelligent human-like behaviors. This goal implies that machines will be able to recognize any visual setting, comprehend any text written using a natural language format, or perform a coherent activity within our physical world. In the 1950s, AI frontier-person Arthur Samuel defined machine learning as the field of study that allows computers to grasp concepts without explicitly being directed to or programmed.
Machine learning comprises four subcategories:
Supervised machine learning models are trained using labeled data sets. For example, an ML algorithm would be conditioned using pictures of cats and other items - labeled by humans - and the learning procedure would train models to identify pictures of cats by themselves. Supervised ML modeling is the most common type of ML used today.
Unsupervised machine learning modeling is where a program examines multiple patterns of unlabeled data. Unsupervised ML models will find patterns or trends that people are not unequivocally searching for. For example, an unsupervised ML program can search through gigabytes of online sales data and determine those diverse client types who are making specific purchases.
Reinforcement machine learning modeling will train or teach machines through trial-and-error mode. The goal is to abide by the most appropriate course of action to maximize an established reward evaluation. For example, reinforcement learning can train ML models to teach self-guided autonomous vehicles how to drive by conveying to the machine when it has made the correct decisions. Those correct decisions will dictate the driving course of action to abide by over time.
Semi-supervised machine learning algorithms comprise models trained using a small amount of labeled data and a large amount of unlabeled data. After being trained or prepared using labeled data, a semi-supervised ML model will have the ability to label the remainder of the larger dataset and use this newly labeled data to train a better model. Such ML algorithms are applied for specific tasks such as web content classification and speech analysis.
Examples of the type of ML algorithms used are as follows:
Decision Tree algorithm,
Gradient Boosted Trees.
Discover how training data can make or break your AI projects, and how to implement the Data Centric AI philosophy in your ML projects.
What Exactly is Deep Learning?
Deep learning networks are classified as neural networks with multiple layers. This layered network is generally trained on vast amounts of data to obtain each neuron link's weighting within the network.
For example, in a computer vision recognition system, some layers of the neural network will witness individual features of a face, such as eyes, mouth, or nose, while other network layers will be tasked with combining those previous layered features to determine if a face appears in a typical fashion.
Deep learning is modeled on how the human brain functions, and it powers numerous machine learning models, such as autonomous vehicles and medical diagnostics. The more layers a DL model has, the greater its potential to successfully perform complex tasks.
Neural networks are a specific class of machine learning algorithms. In an artificial neural network, cells are interconnected, and they process inputs and generate outputs, which are then sent to other cells within the network. Input data moves through the cells, with each one performing a distinct function.
Deep learning demands a tremendous amount of computing power to train from large datasets, and the high costs associated with the required computing power tend to limit these big training procedures to big companies. However, recent advances in Transfer Learning now make industrial projects tractable by requiring only a few examples to provide reasonable performance. Transfer Learning consists in taking models that have already been trained on large datasets (base models) and then fine-tuning them on smaller datasets to obtain acceptable results at a fraction of the base model training cost.
Common examples of DL applications are as follows:
Driverless Auto-guided vehicles,
Virtual Digital Assistants (such as Alexa, Siri, and Cortana),
Digital Facial Recognition,
Language Translation software
Aerospace and Defense.
Examples of the type of DL algorithms used are as follows:
Recurrent Neural Networks,
Convolutional Neural Network,
Long Short-Term Memory Networks,
Deep Boltzmann Machine,
Deep Belief Networks,
The Main Differences between Machine Learning and Deep Learning
While deep learning is a subset of machine learning, there are stark differences between the two AI algorithmic learning approaches.
Algorithmic Processing Time
As one might expect, due to the large datasets that a deep learning model requires and the numerous hyperparameters and complicated mathematical formulas applied, a deep learning system needs time to train sufficiently. As a result, deep learning models can take weeks to finish successfully. In contrast, machine learning model processing takes considerably less time - from seconds to hours to complete.
Performance and Growth
The computation capabilities of Graphical Processing Units (GPUs) have permitted the training of large neural networks, which has yielded successful challenging project outcomes. In contrast, other formats of ML have reached performance plateaus.
Machine learning algorithms tend to parse input data into pieces, and then those components are serially merged to produce a desired result or solution. Alternatively, deep learning systems scrutinize an entire situation or scenario in one whole action or
For example, suppose a data scientist wanted to create a learning model to identify individual objects within digital images (such as car license plates in a parking lot). If machine learning was to be employed, then there are two key processes required to complete this task:
Object detection, and
If the data scientist wanted to employ a deep learning model to solve the same task, they would need the input image and the proper model training. After processing, the DL model would return both the identified objects and their location within the image - all in one discrete activity.
Vanilla machine learning and deep learning systems are used for different applications, given the differences mentioned above.
Vanilla machine learning applications comprise predictive-like programs, for example,
Forecasting price rises or drops within the stock market,
Predicting adverse weather patterns, such as when and where a hurricane might strike,
Identifying spam from regular email, and
Evidence-based treatment plans for seriously ill patients.
One highly publicized application of a deep learning model is self-guided or self-driving cars. These models and programs use multiple neural layers and networks to determine objects to avoid, how to recognize and obey traffic lights and when to slow down or speed up.
The following table summarizes the key differences between machine learning and deep learning models.
|Data||Requires large volumes of data and datasets, unless Transfer Learning can be used.||Good results are obtained with small data and datasets.|
|Time||Takes longer to train, and execution time can take weeks unless Transfer Learning can be used.||Takes less time to train – from minutes to hours only.|
|Output||Produces high-quality output. The output can take any shape, including free-form components such as text and sound.||Provides lesser accuracy output than what DL produces. ML produces categorization and scoring results, which are primarily numerical.|
|Utilizes||As a minimum of three neural layers, but typically use much more.||When neural networks are used, they can consist of one neural layer t.|
|Complexity||Quite complex due to the number of neurons.||Generally smaller model...|
|Hardware||Computationally intensive, requiring powerful resources (or GPUs).||Can adequately train models using hardware such as a standard PC or Mac CPU.|
|Project Cost||DL projects are quite costly, primarily due to the expensive resources required (hardware, human resources, etc.), and the length of time used for successful model processing.||ML projects are less costly as the machines, and associated processing time, are considerably fewer than DL project processing requirements.|
Table: Key differences between Deep Learning and Machine Learning
If we take a step back and recap, the main differences between deep learning and machine learning are:
the model complexity: DL models always involve a large number of parameters (and consequently higher costs), while ML models are usually simpler.
Vanilla Machine learning algorithms are usually enough to handle structured datasets, composed of a limited number of self-explanatory numerical and categorical characteristics, while the learning capacity of deep learning networks can handle unstructured data like images, audio, video….
In conclusion, we can state that deep learning is machine learning but with significant additional capabilities utilizing a distinct operational approach. Choosing either ML or DL to unravel a particular data model challenge depends on the complexity of the issue at hand and the amount and quality of data available.
Recently, artificial intelligence has successfully integrated into our daily lives. For example, these technologies can successfully recommend shows, movies, and books. They also suggest nutritional meals and customize our end-user social network. In a nutshell, they can predict and influence consumer behavior.
Their abilities will change almost every sector that we know and live within. Simultaneously, individuals will look to utilize AI to supply fresh entertainment experiences - such as the Metaverse.
AI refers to machines demonstrating human-like intelligence in some manner. While AI has multiple techniques, one key subset is machine learning – where the model lets the algorithm learn from data. And finally, deep learning is a further subset of machine learning, using multi-layered neural networks to crack the most challenging hardest of situations.
The development of machine learning, and in particular - deep learning - has inspired data scientists to take large advancement steps toward real-world AI applications. As AI technology matures, it becomes essential to understand the pivotal differences between these advanced core concepts.