If you think that Deep Learning (DL) and Machine Learning (ML) have a lot in common, you’re right. Both terms are related and fall under the category of Artificial Intelligence (AI).
But if you heard someone using Deep Learning as a synonym for Machine Learning, it’s not quite accurate. Deep Learning is a subset of Machine Learning, but they’re not the same thing.
What is Machine Learning?
Machine Learning is the use of historical troves of data, and statistical algorithms trained on that data, to build systems that identify patterns and infer future predictions when given new observations. Machine learning solutions can automatically adapt over time based on the new and natural patterns within the data, rather than having to programmatically account for every possibility with logic and routing. There are 3 types of Machine Learning:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
The most common form, supervised learning, refers to algorithms that learn from data labeled by humans. The most common supervised machine learning techniques include linear regression, decision trees, or random forests.
In order for ML algorithms to make decisions, predict something, or recognize a pattern, data scientists have to train them with properly collected, cleaned, engineered, and labeled data. Based on a data team’s data pipeline, model accessibility, and the model’s subsequent exposure to more data, the effectiveness of the algorithm can continue to improve.
Ultimately, a trained machine learning model is composed of the parameters that inform how much each variable affects the predicted value (for example, when predicting home prices, how much does the number of baths sway a home price).
The higher-quality the data the ML algorithm gets, and the more useful the features engineered within the dataset, the better, and more accurate the output will be.
How does Machine Learning work?
Machine learning is the parent category for artificial intelligence methodologies that include deep learning, so the principles by which machine learning works gives some understanding as to how all forms of artificial intelligence products work.
Machine learning relies on examples of past experiences in the form of data to offer predictions with varying levels of confidence in the future. If an experience in the future very closely resembles an example seen by a ML model in the past, it can likely predict an outcome (also referred to as a target) with fair confidence. That space of ML with an object to predict is called supervised learning.
In some more detail now, supervised machine learning works by finding a function (just a combination of inputs and tuning parameters) to fit a dataset where the experiences (data) have a label. The right parameters for the function are the ones that minimize the function’s error rate against that particular set of data; you can think of the error as the distance between a prediction and the actual value. The process of creating that function with minimized error is also referred to as training.
As an example, if a data scientist is trying to train a model to predict home prices, they may use a linear regression model whose parameters like the number of baths, the square footage of the house, and the proximity to a mass transit center affect the label: the home price.
By comparison, when machine learning is not deployed to help predict an outcome, it’s usually used for the purposes of identification or segmentation. Those approaches that lack a variable, or target to predict, are a part of the sphere of unsupervised learning. That brand of machine learning works by grouping similar data points together during training and then determining which group a new and previously unseen sample should fall into.
As an example of unsupervised ML, if a data scientist is trying to train a model for anomaly detection, they may use a clustering model (like K-Means) to see which data points make sense to be clumped together. If there are data points that don’t fit into a cluster, those are the anomalies.
Machine Learning is a part of AI and the goal of using it is to create automated capabilities that can enhance, scale, and replicate the kinds of thinking people are so capable of on their own; specifically AI is used to provide the flexibility that programmatic/deterministic software solutions can’t deliver.
What is Deep Learning?
Deep Learning is a Machine Learning technique that draws its inspiration from the human brain and how it thinks and extracts information. Deep Learning models are often viewed as synonymous with Artificial Neural Networks (ANN) which resemble brain structure and function.
As compared to more traditional models, you can think about Neural Networks as models whose parameters are contained in different layers and nodes. Those layers are set in an order. When the first layer receives raw data (input), each layer can learn something new from the previous layer’s output.
While neural networks are seen as a black box model (it’s not 100% clear what parameters ultimately produce the output predicted) it’s fair to say that certain regions of weights and nodes activate in a principle similar to the parameters of a traditional machine learning model.
Deep learning models are especially good at using data to recognize patterns in forms like images or documents and can identify abstract objects.
How does Deep Learning work?
Deep learning works to recognize patterns and relationships in data and is often used for processing data formats like documents, photos, videos, or audio. “ Deep” is to indicate that learning happens in several layers. Say the first layer learns to identify an “orange” and its basic features in an image. By going through the next layers, the model may add information about more features that make an orange an orange (texture, color, shape, etc). Each new layer has more information about the features of the orange based on the previous layers’ knowledge so it can better detect the fruit and differentiate it, for example from an orange ball.
How accurate it is at categorizing each item it sees (the orange), and therefore how quickly it’s learning is determined by comparing the predicted value to the correct value. Comparing the two values and then feeding the results back into the training process gives the neural network the chance to experiment with another set of weights and biases.
For another scenario, say you’d like to build an ML algorithm that can distinguish between an image of a cookie and an image of a dog. To do this you’d need a lot of training data, in this case, images that present either a dog or a cookie and are labeled appropriately.
By training the ML algorithm on what features have dogs and what features belong to cookies, the algorithm learns to recognize whether it’s a dog or a cookie and uses this information to predict the correct label for the new image.
Key differences between Machine Learning and Deep Learning
After all, it is all Artificial Intelligence
Although the concepts have their own nuances that delineate where and when to apply each, don’t forget that Deep Learning is actually part of Machine Learning. They each represent specific approaches to automating tasks with specialties based on type of data, but both are intended to make life easier.
Despite popular usage of platforms like ChatGPT, the utmost benefit of AI is in making software applications more intelligent and finding patterns where humans wouldn’t be able to in huge troves of data.
So, what’s better: Deep Learning or Machine Learning?
The truth is, your use case should dictate whether you’ll use Deep Learning or simpler Machine Learning models. More traditional machine learning models are comparatively cheap and easy to train and deploy, while deep learning models help automate use cases for data types like image.
Say you have well-structured, clean numerical data and you’d like to predict customer churn in your insurance company or classify your customers and their lifetime value. In this case, building and training a simpler Machine Learning model like a logistic regression is a better choice.
But if you’re dealing with unstructured data types and need to do image recognition, Deep Learning will do the job here. Say you’re giving a DL algorithm an image of a dog but you’re not telling the model what the picture presents. Here, the neural network will recognize, step-by-step, features of a dog, until it classifies the image as a dog image.
Ushur’s AI: the Best of Both Worlds
Ushur’s Customer Experience Automation (CXA) Platform is designed to support the operations of complex businesses in the insurance, healthcare, and financial industries. Ushur and Ushur AI Labs have already thought through the decisions an enterprise business needs to consider when evaluating AI projects in highly regulated industries. Using proprietary language and document services, Ushur’s AI solutions help businesses understand user intent, evaluate document content, and drive seamless customer experiences so that customer support functions and business owners can focus on the highest value projects in their queues.
If you want more information on the machine learning and deep learning capabilities in the ushur platform, and to see how Ushur blends AI with customer experience technologies visit ushur.com/platform.
