Technical Articles & Industry Trends

Deep Learning & Machine Learning: What Are the Differences?

In today’s tech world, talk about Artificial Intelligence (AI) and how it’s changing the industry is all the rage. Much like IoT in the mid-2010s or “the internet” (as it was understood at the time) in the 90s, AI is used so much in today’s media, and, for most, it only represents a vague idea or is associated with popular tools such as ChatGPT or Dall-E.

In this article, we will add some definition and context to the concept of AI by breaking it into two distinct parts: deep learning & machine learning.

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How to Define AI

A popular function of AI can be described as a set of algorithms that perform reinforced learning with human feedback. Machine learning (ML) and Deep Learning (DL) represent the different levels of complexity those algorithms can possess.

AI is the large umbrella where this learning takes place, while machine learning was the first description of these algorithms under the AI Umbrella. Further, deep learning is a subcategory of machine learning.

So, what distinguishes these categories from each other? The answer is found by breaking down the AI learning process.

The AI Learning Process

People approach solving new problems by using what they already know to make guesses about the environment, and subsequently learn based on whether that guess is right or wrong.

For example, you are probably very certain the image on the right is the front of a camera because you have likely seen the front of a camera before, and context leads you to that conclusion. That assertion is correct and therefore you have increased your ability to recognize a similar picture in the future as you have an additional correct example from which to reference.

In AI terms, this process of problem solving by using context and what is already known is called inferencing, as opposed to learning through feedback and repetition which is called training.

Inferencing vs. Training

Inferencing and training are the two main components in both deep and machine learning. Both inferencing and training across DL and ML algorithms start with an input layer from which the data comes. For humans, this input layer is our five senses. For sight, this is the data collection that occurs at the back of your eye as light hits your rods and cones and is converted into meaningful electrical signals that your brain can process.

In the case of both deep & machine learning, these electrical signals are represented as numbers that are assigned to every data point that comes in. For imaging, the AI equivalent of sight, the algorithm is associating a number to every individual image pixel that is inputted.

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Where the Difference Makes All the Difference

In both the brain and the AI algorithm, the data collected at the input layer is then sent away to be intensely scrutinized. That is where the similarities between the brain, AI, deep learning and machine learning ends, however.

In the human brain, sensory data is sent to be interpreted by billions of neurons in which training and inferencing happen virtually simultaneously, whereas AI mostly requires separate occasions for inferencing and training. These inferencing and training processes require that data must be passed through what are called hidden layers which essentially act as neurons in a processing capacity. Machine learning algorithms have only one of these layers, while deep learning algorithms possess multiple.

Deep learning has multiple hidden layers while machine learning has only one. This is the main difference between deep learning and machine learning. Deep learning, therefore, more closely mimics the human brain and can handle much more complex tasks with far greater accuracy. When you think of AI today, you are most likely thinking of something that leverages deep learning such as the afore mention ChatGPT, Dall-E, or even the virtual assistant on your phone.

"Deep learning has multiple hidden layers vs machine learning, which only has one. This is the main difference between deep learning and machine learning. Deep learning, therefore, more closely mimics the human brain and is able to handle much more complex tasks with far greater accuracy."

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ML & DL Outputs

Depending on whether training or inferencing is taking place, the process stops with a few outcomes. While training is taking place, the whole cycle is repeated after the mathematical interpretation, or the “weight” of the data, is updated based on the results.

In human terms this is what happens when you learn to recognize a specific object such as a type of plant. After practicing and reading you learn to pay close attention to certain attributes of the plant such as the leaf shape or size and not pay attention to others such as the color or shape of the stem. This process will repeat itself thousands of times until the algorithm ceases to get more accurate when shown similar data.

For inferencing the output is much more straight forward as it is whatever the model is designed for. A Large Language Model (a massive collection of algorithms trained and built around language) such as ChatGPT, will output language, and a Transformer Language Model (A massive collection of algorithms trained to take language and transform it into another state) such as Dall-E, will output images or actions. Because machine learning is limited to one hidden layer the output and therefore applications will be similarly limited. Machine learning outputs can be numbers, statistics, or images via a process known as machine vision, whereas deep learning outputs, depending on the use case, can range from complex language to original images or to informed action on an assembly line.

Teguar is continuing to explore and develop AI solutions as well as integrate AI into our business processes. Click to see industrial and medical computers that are ready to tackle AI applications and fill out the form below or reach out via chat to discuss how Teguar can enable your AI project.

FAQs

Is deep learning a subcategory of machine learning? Yes, deep learning is a subcategory of machine learning that has more hidden neural layers.

Is deep learning the same thing as machine learning? Mostly yes as the inherent process is similar, but deep learning has more hidden neural layers, making it a lot more powerful.

What are DNNs and LLMs and what do they have to do with deep and machine learning? DNN or Deep Neural Networks are neural networks that use deep learning, and LLMs or Large Language Models are a form of a DNN that is concerned with language as an output.

What is a neural network? A neural network is a collection of algorithms and processing nodes that are designed to mimic the way a brain uses neurons to process and retain data.

Is machine learning becoming obsolete? Machine learning still has many uses, but as more is expected from AI, deep learning and therefore more powerful hardware and larger inputs, will be required.

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