What Is the Difference Between Machine Learning and Deep Learning?

Machine learning and deep learning are distinct disciplines of artificial intelligence (AI). The two technologies share many characteristics as they leverage computing power to simulate and augment human intelligence. Key differences in these AI solutions make them appropriate for addressing different classes of tasks and automated processes.

Machine learning (ML) is a broad category that relies on dedicated algorithms and statistical models to automate specific processes without explicit instructions and with less reliance on human intervention. Deep learning (DL) is a subset of machine learning using neural networks to learn or draw insights from large amounts of data. Essentially, all deep learning is machine learning, but all machine learning is not deep learning.

This article discusses the differences between machine learning and deep learning. We will look at the underlying technologies used to provide ML and DL applications with their intelligence and how they are trained to address specific problems. We’ll also investigate how these technologies are responsible for major changes in how businesses and the general public interact with computing systems.

What Is Machine Learning?

Machine learning is a discipline of artificial intelligence that concentrates on developing applications and computer systems that can perform complex tasks they have not been explicitly programmed to accomplish. ML systems learn from exposure to training data that they use to make informed decisions or predictions. The system’s accuracy increases as the learning process progresses and additional data is ingested into an ML model.

ML systems enable computers to perform tasks that require human intelligence without explicit programming. The systems employ techniques like linear regression and decision trees for classification and clustering activities. ML models are more effective when working with small datasets. The data used by ML models may need to undergo manual preprocessing by domain experts.

Key Components of Machine Learning

Several key components work together to form the foundation of machine learning systems. Each contributes equally to the accuracy and utility of the resulting system or application.

Data

Data is a required raw material for ML systems. Both structured data such as tables or unstructured data can be used as ML inputs. Unstructured data may include images, audio, or text to train a model to identify objects. Large amounts of data are typically used to train an ML system.

Algorithms

Multiple mathematical models, or algorithms, facilitate the learning necessary to provide functionality to an ML application. The algorithms are trained to identify patterns and relationships in the data. Historical data is processed to perform classification, make predictions, aggregate data points, and generate original content. We’ll look more closely at the various types of ML algorithms later in this article.

Training

An ML model learns by being trained using the previously described data and algorithms. The model is repeatedly fed training data utilizing its algorithms. Accuracy is improved by adjusting the model’s parameters based on its responses to the training data.

Evaluation and Tuning

After training and testing the ML model, its performance is evaluated using new data. The objective is to determine if the model meets expectations or needs further training before deployment.

Machine Learning Algorithms

Different types of machine learning algorithms are used to train ML systems. These algorithms typically fall into one of the following four categories.

Supervised Learning Algorithms

Supervised learning algorithms teach a model by example. An operator presents the machine learning algorithm with a test dataset including desired inputs and outputs. The algorithm attempts to produce those inputs and outputs by recognizing patterns in the data and making observations and predictions. The operator makes necessary corrections until the algorithm attains the desired level of accuracy.

Supervised learning includes a model to address the following types of tasks.

• Classification – The ML algorithm evaluates observed values and assigns new elements to an existing category. An example is an email filtering system distinguishing between spam and legitimate correspondence.
• Regression – Regression tasks require the ML algorithm to understand the relationship between a single dependent variable and multiple changing variables. Regression is useful for forecasting and making predictions.
• Forecasting – These tasks involve making predictions regarding the future based on past and present data. Forecasting is often used to analyze trends to influence business decisions.

Semi-Supervised Learning

Semi-supervised learning is similar to supervised learning with one exception. Training employs labeled data with meaningful tags and unlabeled data without this descriptive information. The goal is to enable the model to label unlabeled data.

Unsupervised Learning Algorithms

Unsupervised learning involves an ML algorithm analyzing data to identify relationships and correlations without intervention by a human operator. The algorithm interprets large data sets and organizes the information to describe its logical structure. The algorithm improves its decision-making ability as the volume of assessed data increases. Applications of unsupervised learning include:

• Clustering – Segments data into groups based on defined criteria through analysis.
• Dimension reduction – The number of variables under consideration is reduced to focus on the desired outcomes.

Reinforcement Learning Algorithms

In reinforcement learning, the algorithm is given a set of actions, parameters, and outcomes. The model defines rules and explores alternate options to identify the best action to address the problem. The algorithm is taught by trial and error, leveraging past experiences to adapt its responses to achieve optimal results.

Applications of Machine Learning

Machine learning systems are found in multiple usage scenarios across many diverse fields.

• Natural language processing (NLP) – ML systems can be trained to converse with humans using natural language. Intelligent chatbots can provide automated customer service or translation services. They can be instrumental in providing information about an organization’s products and services.
• Speech recognition – Machine learning is the technology responsible for the speech recognition intelligent virtual assistants employ to respond to verbal queries. ML systems can also create written transcripts from human speech.
• Robotics – Robotics is an essential component of industrial automation. ML systems enable industrial robots to perform complex tasks that previously required human intelligence. Robots are used to staff assembly lines and engage in activities such as picking stock items for shipping. Delivery drones are another emerging use of ML-powered robotics.
• Healthcare – Healthcare professionals utilize ML models trained to make recommendations regarding diagnosis and treatment options to enhance and streamline patient care. Another application in the healthcare field is the development of systems that can identify potential diseases or abnormalities from medical imaging.
• Autonomous vehicles – Machine learning systems are powering the development of autonomous vehicles such as self-driving cars. These systems require substantial computing power to address the constantly evolving conditions faced when operating a vehicle.
• Recommendation systems – Many ecommerce and entertainment sites utilize recommendation systems to create a more personalized user experience. Recommendations are made based on a user’s past interactions with the system that allow an ML model to learn about their preferences.

What Is Deep Learning?

Deep learning is a subset of machine learning that employs many-layered (deep) neural networks to learn from large and complex volumes of data. DL systems leverage complex architectures such as convolutional and recurrent neural networks. Deep learning techniques strive to replicate the operation of the human brain.

Deep learning models automatically extract features from raw data to minimize the need for manual preprocessing. They can learn from their own errors and refine their performance over time. Large data sets are essential for developing accurate deep learning solutions. Deep learning algorithms can handle unstructured data more effectively than ML solutions that perform better with structured data.

Deep Learning Key Characteristics

Deep learning systems and applications typically share several key characteristics that differentiate them from machine learning models.

• Artificial neural networks form the foundation of DL solutions. An artificial neural network is made of multiple interconnected layers of nodes. Data is transformed into more abstract representations as it traverses the layers.
• The depth of a deep learning application refers to the number of layers in the network. Most DL models have three or more layers consisting of an input layer, an output layer, and multiple hidden layers. Additional layers enable the system to learn more complex features.
• Deep learning automatically extracts features from unstructured data without the need for preprocessing or feature engineering. A DL model can successfully process large datasets comprised of text, images, and audio elements. More data typically results in improved model performance.
• DL systems need substantial computational power to process the large amounts of data they require. The artificial neural networks use backpropagation to improve the model over time by adjusting the network’s connection weights based on output errors.

Types of Neural Networks Employed in Deep Learning

Different types of neural networks are used to develop deep learning solutions. The following three kinds of neural networks are essential components of deep learning technology.

Feedforward Neural Networks

A feedforward neural network is a simple network in which data moves in one direction from the model’s input layer to its output layer. Data never moves backward to be reanalyzed by the model. Data is fed to the model to train it to make predictions about other data sets. For example, feedforward neural networks are trained to detect fraud and are used in the banking industry.

Convolutional Neural Networks (CNN)

Convolutional neural networks are designed to replicate the visual capabilities of the human brain to process images. These networks are excellent at identifying animal or plant species from photographs. They can also be used to diagnose diseases from medical scans or identify specific items from social media feeds.

Recurrent Neural Networks

Recurrent neural networks employ complex connections that include loops. Data can move forward and loop back to be reanalyzed by previous layers. Each input is fed into the model to be analyzed alone and combined with prior input. Recurrent neural networks can predict sentiments and the ending of a sequence such as a speech.

Applications of Deep Learning Algorithms and Systems

Deep learning has applications in many areas of society and the business world.

• Natural language processing – Deep learning models are powering advanced NLP solutions such as OpenAI’s GPT products. Systems can analyze customer sentiment in response to an organization’s products or services to facilitate personalized marketing. Deep learning solutions furnish enhanced functionality in comparison to ML-powered NLP applications.
• Computer vision – Deep learning models are used to provide applications with computer vision capabilities. This functionality can be leveraged to perform activities ranging from image classification and object detection to facial recognition. These capabilities are essential in many fields such as security, surveillance, and advanced automation as seen in autonomous vehicles.
• Predictive analytics – Deep learning methods analyze vast amounts of historical data from which they can make accurate predictions. This capacity can be used to enhance decision-making, streamline manufacturing operations, and forecast customer trends.
• Smart cities – Deep learning solutions have many applications in creating smart cities that provide society with improved living conditions. Examples are surveillance data used to identify dangerous incidents and traffic management to minimize congestion in crowded metropolitan areas.
• Financial services – DL is being used by financial services companies to detect and prevent fraud. Organizations are also employing deep learning models to predict and analyze market trends and execute trades quickly to address evolving conditions.

Advantages and Challenges of Deep Learning

Deep learning technology offers multiple advantages and presents several challenges that must be effectively addressed.

Benefits

• Automated feature extraction enhances productivity by minimizing manual feature engineering.
• Deep learning systems are flexible and versatile due to their ability to process all types of unstructured data.
• Systems exhibit high accuracy when operating with complex data.

Challenges

• Training deep learning solutions requires large datasets. This can pose difficulties and hinder the use of DL in some applications.
• Deep learning applications consume substantial computational resources that can present financial constraints. Specialized hardware such as a graphics processing unit (GPU) may need to be used to support deep learning solutions.
• Transparency may be an issue with deep learning systems that appear to be black boxes to a typical user.

Deep Learning vs. Machine Learning

The following chart illustrates the main differences when comparing deep learning vs. machine learning.

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