Difference Between Machine Learning & Deep Learning
Machine Learning (ML) and Deep Learning (DL) are two fundamental aspects of the field of Artificial Intelligence. Together, they have helped develop many of our everyday tools in the modern digital world, from social media recommendation feeds to self-driving vehicles and image recognition filters on smartphones.
Both ML and DL are integral to the advancement of AI, helping to create stronger algorithm models, but their similarities can overlap sometimes, so what is the difference between Machine Learning and Deep Learning?
Machine Learning: An Overview
Machine Learning is a subset of AI that uses algorithms and statistical models to assist computers in performing tasks without explicit instructions. Instead, ML models rely on patterns and data feeds to analyze and determine their output.
ML first began as a concept in the mid-20th century when it was originally coined by Arthur Samuel in 1959 while working at IBM. However, further study of Machine Learning would see major advancements until the 1980s when hardware technology was more capable.
With the internet’s introduction to society shortly after, Machine Learning was able to analyze new scales of data, leading to the “Big Data” trend in tech shortly after which has been able to monetize online user data. This new integration with the internet brought on a new world of applications for Machine Learning.
Core components of Machine Learning include:
Data: The fundamental resource for machine learning, data provides the basis for the model’s ability to learn and make decisions.
Model: The specific system or algorithm being used by a machine. Different types of models exist including linear regression and decision trees.
Features: Measurable properties or characteristics of the observation. The quality of a feature can have a major impact on ML models.
Learning Algorithm: The process a machine uses to adjust its parameters, optimizing weights and bias.
Evaluation: A concluding step for the machine to assess its performance, being told whether it made the correct decisions or not.
Deep Learning: An Overview
Deep Learning is a subset of Machine Learning that focuses on creating algorithms inspired by the human brain using neural networks. These neural networks contain a set of hidden layers with interconnected nodes between the input and output layer that cause the model to become ‘deep.’
First developed in the late 1980s and early 1990s using early artificial neural networks. These early examples of DL were possible following breakthroughs in backpropagation and convolutional networks. However, real innovation in DL didn’t fully begin until the late 2000s when technology like GPUs became more advanced and large data streams from the internet became accessible.
Core components of Machine Learning include:
Neural Networks: An AI model that mimics the human brain, consisting of an input and output layer with hidden layers in between for data to travel through.
Weights and Bias: The parameters used by a DL model to help with its decision-making. Adjustments are made after each training cycle.
Activation Function: A function that decides whether a specific neuron in the network should be activated by the weights and bias.
Loss Function: A function used to help improve accuracy within the model’s output, helping the machine reach its target output.
Optimizer: An algorithm used to help the machine adjust its parameters to minimize the loss function.
Comparison of Machine Learning and Deep Learning
There are many similarities between machine and deep learning due to their relationship with AI. They both rely on data feeds to help create predictions and decisions, using their outputs to evaluate themselves in order to produce more accurate results in the future.
Over time, both ML and DL models use algorithms that improve over time. This leads to a wide range of applications across multiple sectors, but there are many differences too:
Concept and Approach: ML models take a more straightforward approach than DL models, using a function that helps identify input and output variables while DL models rely on more complex neural networks to process information.
Data Requirements: DL models tend to use much larger amounts of data than ML models because DL models use a larger amount of parameters than ML models which require additional training.
Computational Requirements: ML models can be trained on low-end hardware because they don’t use neural networks which are more resource intensive. DL models, on the other hand, require additional components like GPUs to work successfully.
Interpretability and Transparency: ML models often use more simplistic algorithms like linear regression which are easy for humans to interpret while most DL models are referred to as a black box because their decision-making process isn’t always transparent to developers, spurring the need for Explainable AI.
Performance and Application: ML models do best on tasks that are well-defined, whereas a DL model's strengths lie in more advanced tasks like recognition and detection which require additional techniques like computer vision.
Despite their similarities, the differences between Machine Learning and Deep Learning are not small, with DL models requiring more advanced hardware and resources. However, that doesn’t mean that Deep Learning is superior to Machine Learning. Instead, they are two aspects of AI that help to lift each other up in order to produce new insights into artificial intelligence.
