You’ve likely encountered the terms artificial intelligence (AI), machine learning (ML), and deep learning (DL) frequently. While interconnected, each possesses a unique definition, extending beyond buzzwords used solely for self-driving cars.
In essence, deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence. Visualize them as overlapping circles, with AI being the largest, followed by machine learning, and then deep learning. Therefore, deep learning is AI, but AI isn’t necessarily deep learning.
This guide will delve into AI, machine learning, and deep learning, exploring their relationships and differences. If you aspire to develop your machine learning skills, consider exploring specializations offered by leading institutions.
Deep Learning vs. Machine Learning: Key Differences
Fueled by popular culture portrayals, many have some understanding of AI. AI is defined as “the theory and development of computer systems able to perform tasks that normally require human intelligence.”
Machine learning and deep learning are both forms of AI. Simply put, machine learning is AI that can adapt autonomously with minimal human intervention. Deep learning is a specialized subset of machine learning that employs artificial neural networks (ANNs) to simulate the human brain’s learning process.
Let’s examine their core distinctions before proceeding:
Venn diagram illustrating the nested relationship between deep learning, machine learning, and AI.
| Feature | Machine Learning | Deep Learning |
|---|---|---|
| Relationship | Subset of AI | Subset of Machine Learning |
| Data Requirements | Can train on smaller datasets | Requires vast amounts of data |
| Human Intervention | More intervention needed for correction & learning | Learns autonomously from environment & past errors |
| Training & Accuracy | Faster training, lower accuracy | Slower training, higher accuracy |
| Correlation Analysis | Simple, linear correlations | Non-linear, complex correlations |
| Hardware | Can train on CPU | Needs a specialized GPU for training |
Understanding Artificial Intelligence (AI)
At its core, artificial intelligence leverages computer science and data to enable machines to solve problems.
While we don’t yet have sentient robots dominating the world, AI permeates our daily lives. Examples range from computer programs that play chess to sophisticated algorithms that predict the RNA structure of viruses to accelerate vaccine development.
For a machine to improve independently without human intervention, machine learning is essential.
Case Study: Deep Blue and Traditional AI
Before machine learning, AI machines had to be explicitly programmed to react to specific inputs. Deep Blue, the chess-playing computer that defeated a world champion in 1997, determined its moves based on a vast library of possible scenarios. The system was fundamentally reactive. To enhance Deep Blue’s chess abilities, programmers had to manually add more features and possibilities.
Exploring Machine Learning
Machine learning is the study of computer systems that learn and adapt automatically from experience without explicit programming.
With basic AI, programmers manually code each decision, instructing the machine how to respond to various inputs. With machine learning, data scientists “train” a machine by feeding it massive datasets. The machine follows a set of rules—an algorithm—to analyze and extract insights from this data. The more data it processes, the better it becomes at a given task.
Consider how Spotify learns your musical tastes to provide recommendations. Each time you listen to a song in its entirety or add it to your library, Spotify updates its algorithms to refine its suggestions. Netflix and Amazon employ similar algorithms to deliver personalized recommendations.
Case Study: IBM Watson and Machine Learning
In 2011, IBM Watson outperformed two Jeopardy champions in an exhibition match using machine learning techniques.
Watson’s developers provided it with thousands of question-answer pairs and examples of correct answers. Given an answer, Watson was programmed to generate the corresponding question. If wrong, programmers corrected the machine, enabling it to modify its algorithms and “learn” from its mistakes.
By the time Watson faced the Jeopardy champions, it could process 200 million pages of information in seconds and generate a ranked list of likely answers, even if it had never encountered the specific Jeopardy clue before.
Diving into Deep Learning
While machine learning algorithms typically require human correction when mistakes occur, deep learning algorithms can refine their outputs through repetition without intervention. Machine learning can learn from relatively small datasets; deep learning requires big data, potentially including diverse and unstructured information.
Consider deep learning as an advanced form of machine learning. Deep learning is a machine learning technique that layers algorithms and computing units—neurons—into an artificial neural network. These networks are inspired by the structure of the human brain. Data traverses this web of interconnected algorithms non-linearly, mimicking how our brains process information.
Case Study: AlphaGo and Deep Learning Mastery
AlphaGo made history as the first program to defeat a human Go player, also becoming the first to defeat a Go world champion in 2015. Go, an ancient board game originating in China, is known for its complexity. It’s significantly more complicated than chess, featuring 10 to the power of 170 possible board configurations.
AlphaGo’s creators started by exposing the program to numerous Go games to teach it the mechanics. It then played against itself thousands of times, learning from its mistakes. AlphaGo became so adept that human players began studying its innovative moves.
The latest version of the AlphaGo algorithm, MuZero, can master games like Go, chess, and Atari without even being provided the rules.
The Significance of Big Data in Deep Learning
Big data refers to datasets too large for traditional databases and data processing software to manage. Businesses generate unprecedented volumes of data daily, and deep learning offers a way to extract value from it.
Develop Your Machine Learning and Deep Learning Skills
AI advancements are transforming how many of us work. Build foundational machine learning and deep learning expertise with the following resources:
To gain an overview of AI: Consider taking an introductory course that covers AI terminology, project building, and AI strategy implementation.
To master core AI concepts and practical machine learning skills: Enroll in a comprehensive machine learning specialization.
To learn the fundamentals of deep learning: Explore a specialized deep learning course that teaches how to build and train deep neural networks and identify critical parameters.
Frequently Asked Questions (FAQ)
Where does machine learning typically fit in the broader landscape of technology?
Machine learning typically falls under the scope of data science. A foundational understanding of its tools and concepts can help you advance in this field or pursue a career as a data scientist.
How long does it take to learn machine learning?
Because machine learning is a dynamic field, learning is continuous. Depending on your background and learning pace, it might take weeks, months, or a year to build a solid foundation.
Is machine learning difficult to learn?
The technical skills and concepts can be challenging initially. Breaking it down into smaller learning pathways and committing to daily learning can make it achievable. You don’t need to master deep learning or machine learning to apply your skills practically.
Do I need to be an expert programmer to work in machine learning?
Deep learning and machine learning as a service platforms enable you to build models, train, deploy, and manage programs without extensive coding. While programming expertise isn’t strictly required, basic proficiency in Python is helpful.
What is the job outlook and salary for machine learning engineers?
Demand for AI and machine learning skills is projected to grow significantly, accompanied by a salary premium.
What is Natural Language Processing (NLP) and how does it relate to machine learning?
Natural language processing (NLP) is a branch of machine learning that focuses on enabling machines to understand human language. It is used in virtual assistants, chatbots, and speech recognition software.
