13.7. Machine Learning and Deep Learning
Machine Learning
Machine Learning (ML) uses data and algorithms to imitate the way humans learn. Machine learning is used to analyze data and build models without being explicitly programmed to do so
| Algorithms | is a set of instructions for accomplishing a task. |
ML techniques can be subdivided into three categories: supervised learning, unsupervised learning, and reinforcement learning.
| Supervised Learning | Occurs when an organization has data about past activity that has occurred and wants to replicate it. Algorithms are used to classify data based on labels. As an example, supervised learning can use image recognition algorithms to find objects in videos or images. Supervised learning techniques can include decision trees, neural networks, and regression. |
| Unsupervised Learning | Occurs when an organization has data and wants to understand the relationship(s) between different data points. Unsupervised learning uses unlabeled data sets to analyze, cluster, and identify patterns. Unsupervised learning techniques include clustering and association techniques. |
| Reinforcement learning | Uses unlabeled data and learns through a series of trial and error actions. |
Supervised and Unsupervised learning Examples
If a company wants to create a new marketing campaign for a particular product line, they may look at data from past marketing campaigns to see which of their consumers responded most favorably. Once the analysis is done, a machine learning model is created that can be used to identify these new customers. It is called “supervised” learning because we are directing (supervising) the analysis towards a result (in our example: consumers who respond favorably).
If a retailer wants to understand purchasing patterns of its customers, an unsupervised learning model can be developed to find out which products are most often purchased together or how to group their customers by purchase history. Is it called “unsupervised” learning because no specific outcome is expected.
Machine learning models can form the core of logistics and supply chain solutions in terms of optimizing the product packet size, delivery vehicle selection, delivery route selection, and delivery time computation. For instance DHL uses Amazon’s Kiva robotics (improve speed, accuracy) for the network management.
Deep Learning
Deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing and also creates patterns for decision making. Deep learning and neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, and minimize running expenses for hospital management and patients. While machine learning, deep learning, and neural networks are all subsets of artificial intelligence; deep learning is actually a subset of machine learning, and neural networks are a subset of deep learning.
| Neural Networks | Algorithms that process information in a similar way to the human brain. Contain a collection of interconnected nodes. |
Deep learning can be thought of as the automation of predictive analytics. Deep learning is essentially a neural network with three or more layers which allows it to learn from a large amount of data. Deep learning is behind many artificial intelligence applications improving on automation and analytics. It is used in such applications as voice activated electronics, self-driving cars, and credit card fraud detection. The evolution of deep learning started after the invention of neural networks, by adding more neurons and additional hidden layers to neural networks makes deep learning more cultivated. Like machine learning, deep learning is also categorized into subcategories, i.e., supervised, and unsupervised.
Machine Learning vs Deep Learning
Machine learning focuses on developing algorithms that can alter themselves without human involvement to take defined data and generate a required output. Deep learning uses neural networks to learn unsupervised from unstructured or unlabeled data. Machine learning uses algorithms to analyze data, learn from it, and make smart decisions based on the knowledge learned, while deep learning organizes the algorithms into layers to form artificial neural networks that can learn and make intelligent decisions independently.
