Machine Learning vs Deep Learning: Understanding the Differences and Similarities

Artificial intelligence has become so common now that even kids can tell you about it. Speaking with Siri or Chat GPT, using Netflix or Spotify recommendations, and even tracking the fastest, most convenient path with Google Maps—all of these are AI-powered tools. The terms deep learning and machine learning are also widely known, but most people use them interchangeably. That’s not quite right.

So, what are machine learning and deep learning?

These two fields in Data Science are closely related, but are not the same. In this article, I’m going to dive into machine and deep learning, their similarities, and differences.

Spoiler: it’s easier to understand than you can imagine.

What is Machine Learning?

ML is a part of artificial intelligence that allows computer systems to be trained without fully programming them. ML is based on statistics and enables a program to classify data and predict the outcome of complicated simulations by using data-analyzing algorithms and patterns.

Machine learning programmers are focused on developing algorithms that allow computing systems to improve by automatically analyzing massive amounts of information. A program discovers data patterns and links according to pre-written algorithms and creates predictions and decisions even more accurate than traditional programming may provide.

Machine Learning Types

There are three main types of machine learning used in programming:

• Supervised learning

The computer is trained using previously structured and labeled examples. It is used when the output is well-known, so the predictive model uses both the data and the required output. For example, if a developer wants to teach a model to distinguish pictures of humans, they must feed it with labeled images of people.

Here are the most common algorithms using supervised learning:

• decision trees;
• linear regression;
• polynomial regression;
• k-nearest neighbors;
• naive Bayes classifier.

• Unsupervised learning

This type of analysis uses unstructured data to learn patterns. The result of unsupervised learning can’t be known beforehand because the algorithm discovers attributes and categorizes data based on patterns it finds.

For example, if an unsupervised algorithm receives images of different people, it uses attributes to categorize them into groups by skin, hair, eye color, etc.

The most often used algorithms of unsupervised learning are:

• hierarchical clustering;
• k-means clustering;
• fuzzy means;
• partial least squares;
• principal component analysis.
  

Supervised and unsupervised learning techniques can be combined and used together. This approach is called semi-supervised learning. This means an algorithm must structure data using its categories to reach a predetermined outcome.

For example, a model must find humans on images, but only a small amount of data is labeled as ‘human.’

• Reinforcement learning

It is a type of model training that uses experiments to obtain data. At the beginning of defining patterns, an algorithm acts chaotically and makes many errors. However, as the model progresses, it cuts off wrong patterns and focuses on further developing successful experiments. Reinforcement learning uses data of failed and successful patterns to further training. Its loop can be everlasting and depends only on the required computing resources and time needed to improve a model.

Common examples and applications of ML

Machine learning is widely used in everyday products and services. Let’s examine some of them a bit closer.

• Duolingo, a speech recognition feature for language learning

One of the most well-known language learning apps, Duolingo, uses machine learning tools to create an accurate system for recognizing users’ speech. It helps students improve their pronunciation and emphasizes their mistakes and problems when speaking.

The ML system compares users’ recorded audio answers with native speakers’ samples stored in the project’s database. The final score for speaking and conversational exercises depends on how close the user’s language is to native pronunciation.

• AlphaZero, the most powerful chess engine ever

At the end of 2017, the DeepMind team created a brand-new chess engine called AlphaZero that used reinforcement learning (RL) to improve. Developers set the game's rules but didn’t share any opening books or endgame tables.

AlphaZero just played with itself—thousands and thousands of games. For this task, programmers used 5,000 first-generation TPUs. Another 64 second-generation TPUs were used to train and improve the engine.

Only after eight hours of training did AlphaZero become the undisputed champion between chess engines. The algorithm crushed the former champion Stockfish 8 in a tournament of 100 games with 28 wins, 0 losses, and 72 draws. To equal the initial positions of engines, they both played on machines with four TPUs.

Just for comparison, the highest ELO rating ever reached by humans was 2882. Magnus Carlsen, the 16th world champion, reached it in 2014. But even for the most conservative estimate, AlphaZero scored over 4500 ELO points. If AlphaZero plays with Magnus Carlsen at full capacity, it will win 99.999% of the games.

• AI-powered testing assistant

We built an AI-powered testing assistant for our internal development team. The system utilizes custom storage to match test scenarios with changing needs. This cut update time from hours to minutes. Our assistant can automatically identify affected tests and suggest updates for manual and BDD tests.

The assistant’s machine learning capabilities nearly eliminated human error in test maintenance while ensuring comprehensive coverage across the entire test suite.

What is Deep Learning?

So, how is deep learning different from machine learning?

The answer is simple.

Deep learning is an evolution of machine learning. It resembles the structure of human brain neurons by creating specific interrelations with learning algorithms and computing units.

When using machine learning algorithms, developers must control and correct them if the results don’t meet expectations or lack accuracy. However, deep learning tools can upscale outcomes through multiple repetitions without human assistance.

Still, neural networks need a massive amount of data to train. For example, scientists at Stanford University used just one thousand images to train an ML system designed to diagnose skin cancer using photos.

However, to develop a Chat GPT-3 neural network, creators used about 570 gigabytes of text—more than 300 billion words in different languages. The amount of data needed sometimes differs by order of magnitude.

All neural networks have input and output layers—the ones people work with. But there are also hidden layers. This is where magic is done. The more hidden layers, the deeper the neural network is and the longer its training is.

Deep learning tools are non-linear, and even their developers do not know how data is processed in their hidden layers.

Deep Learning Types

There are various deep learning algorithms designed to solve different tasks. Let’s explore the main of them.

• Recurrent Neural Networks (RNNs) are commonly utilized for data sequencing. They use loops to operate on data, which makes them common in working with natural languages, especially in processing and recreating text or speech.
  
• Convolutional Neural Networks (CNNs) are mostly designed to process visual data. Their convolutional layer structure and interoperability allow them to learn spatial hierarchies and easily analyze and create images.
  
• Generative Adversarial Networks (GANs) consist of two or more independent neural networks competing with each other in problem-solving. One network generates new data, while the other predicts whether the created data belongs to the original dataset. GANs are usually used in generative systems to create synthetic data, such as text, images, and even music. They can also help restore missed data, generate training data for other models, and create 3D visual models from 2D data.
  
• Long Short-Term Memory Networks (LSTMs) are subsets of recurrent neural networks specially designed to deal with vanishing gradient problems in training neural networks with gradient-based learning methods and backpropagation. Implemented with RNNs, long short-term memory networks prevent the entire learning system from the training process from slowing or stopping.
  
• Transformers are a newly developed neural network type based on the multi-head attention mechanism. Initially created at Google, transformers are intended to solve problems in natural language processing, computer vision, reinforcement learning, audio, multimodal learning, and robotics. However, these neural networks don’t have recurrent units, so they can be trained much faster than RNNs or LSTMs.

Key examples and real-world applications of DL

• Grammarly, a tool for checking mistakes and improving texts in English

Deep learning helps computers to ‘understand’ natural languages and even improve the text written by humans. Sure, a neural network works only with phrase structures and not with language itself. But the result is incredible—Grammarly finds up to 98% of spelling mistakes and over 85% of inaccurate wording.

Indian scientists researched how Grammarly affects learning English as a second language. They found that students who use Grammarly to create written homework and prepare for exams earn 10-12 points higher marks out of 100 than those who don’t.

Grammarly combines machine learning and deep learning tools, taking the best options from both approaches. Transformers make it possible to sort out overly complicated English grammar with high accuracy.

Interestingly, the system works simultaneously on different language levels: single words and phrases, sentences and paragraphs, and the whole text simultaneously.

• Tesla cars’ self-driving system

In 2022, Tesla presented a new End-To-End deep learning architecture for its cars, opening a brand-new chapter in the design of self-driving vehicles.

The system gathers data continuously through computer vision. In early versions of Tesla’s deep learning system, two neural networks were trained separately. The first one analyzed collected visual data to define objects on the road. Then, after a series of applied rules and formulas, the second NN calculated the trajectories of other vehicles and pedestrians and estimated how to drive the car safely and within traffic rules.

However, the system was imperfect, so developers decided to create an end-to-end neural network that analyzes all data. Interestingly, to make the system safe, creators kept the option to train neural network blocks separately and visualize the calculation output at every level of operation.

• Face-matching app powered by deep neural network

We helped our client implement a deep neural network and solve a complex challenge: finding specific faces among millions of event photos.

The solution's AI algorithm was capable of matching faces across diverse conditions—from different angles, lighting conditions, and even when participants wore hats or facial accessories.

After three months of intensive development, our team successfully created a prototype that could not only detect faces, but match them across multiple photos with high accuracy, going beyond simple facial recognition. The platform scaled to handle over 1.5 million photos during major events, with users downloading more than 100,000 photos in a single event.

This platform simplifies the way event participants access their photos, eliminating the need to manually search through thousands of images.

Deep Learning vs Machine Learning: How Are They Alike?

Deep learning is a subset of machine learning, so they share some methodologies and features. Let’s look at the most significant ones.

• Statistical basis. The mathematical methods of processing data are quite similar in machine learning and deep learning. Both use regression analysis, decision trees, linear algebra, and calculus.
• Gradual improvement. Both ML and DL models depend strongly on the amount of training datasets. The more, the better.
• Wide range of use. Machine learning vs deep learning examples can be used to solve complex problems in any industry, from agriculture to banking. Deep learning and ML solutions solve complex problems across all industries and applications. Moreover, they can deal with complex issues that classical programming tools and apps couldn’t solve.

Key Difference Between Deep Learning and Machine Learning

Despite all the similarities, DL vs ML are not the same thing. Next, we’re asking the question: what is the difference between machine learning and deep learning?

Learning techniques

• Machine Learning. Developers create learning algorithms that process data using math calculations. Most machine learning tools use simple statistics and algebra to make assumptions and predictions.
• Deep Learning. A system discovers data patterns and connections with multiple interactions with data within the established rules. The process is non-linear and more complex than in machine learning tools.
  

Computational resources

• Machine Learning. Generally, ML tools use more computational resources than fully programmed architecture, but only during training.
• Deep Learning. In most cases, it requires an exponentially greater computational power than any other machine learning model. Some even require supercomputer capabilities, a million times more potent than simple servers.
  

Data requirements

• Machine Learning. Developers can train ML models with a minimal amount of data. Sometimes, even a hundred labeled data samples are enough for effective learning.
• Deep Learning. DL neural networks require massive training data—thousands of times more than classical ML tools. There is no upper bound—the more data you give, the more accurate algorithms you get.
  

Model explainability

• Machine Learning. All machine learning algorithms are well-known and revealed, so programmers can fully understand how they analyze data and make certain conclusions and predictions.
• Deep Learning. Even DL tool creators don’t care how models process data inside their hidden layers.
  

Model training time

• Machine Learning. Simple ML models can be trained within hours, especially for small or mid-sized datasets.
• Deep Learning. DL systems require much more learning time than ML models: days or weeks.

Machine Learning or Deep Learning?

Machine vs Deep Learning models share similarities, allowing them to solve complicated problems with AI-powered tools. However, both approaches share some similarities, but they are usually used for radically different and sometimes even opposite tasks.

ML vs DL can also be combined into a solid system for solving complicated problems using natural languages. It’s impossible to say which is better, machine learning or deep learning, because both are highly useful and beneficial.