Have you thought about where the world would be in the next couple of years with the fast pace of deep and machine learning? Deep and machine learning is the way forward, and although they both get used interchangeably, there are conspicuous differences. In this guide, we would see the differences between deep and machine learning.
What is Machine Learning?
With machine learning, computer systems are programmed to learn from data that is input without being continually reprogrammed. In other words, they continuously improve their performance on a task—for example, playing a game—without additional help from a human. Machine learning is being used in a wide range of fields: art, science, finance, healthcare—you name it. And there are different ways of getting machines to learn. Some are simple, such as a basic decision tree, and some are much more complex, involving multiple layers of artificial neural networks. The latter happens in deep learning.
How does Machine Learning work?
The working of machine learning models can be understood by the example of identifying the image of a cat or dog. To identify this, the ML model takes images of both cats and dogs as input, extracts the different features of images such as shape, height, nose, eyes, etc., applies the classification algorithm, and predicts the output.
What are the different types of machine learning?
To dive a bit deeper into the weeds, let’s look at the three main types of machine learning and how they differ from one another.
As you might have guessed from the name, this subset of machine learning requires the most supervision. A computer is given training data and a model for responding to data.
As new data is fed to the computer, a data scientist “supervises” the process by confirming the computer’s accurate responses and correcting the computer’s inaccurate responses.
For example, imagine a programmer is trying to “teach” a computer how to tell the difference between dogs and cats. They would feed the computer model a set of labeled data; in this case, pictures of cats and dogs that are clearly identified. Over time, the model would start recognizing patterns—like that cats have long whiskers or that dogs can smile. Then, the programmer would start feeding the computer unlabeled data (unidentified photos) and test the model on its ability to accurately identify dogs and cats.
Supervised learning involves giving the model all the “correct answers” (labeled data) as a way of teaching it how to identify unlabeled data. It’s like telling someone to read through a bird guide and then using flashcards to test if they’ve learned how to identify different species on their own.
By contrast, unsupervised learning entails feeding the computer only unlabeled data, then letting the model identify the patterns on its own. This machine learning method is usually used in cases where it’s unclear what the results will look like, so you need the computer to dig through the hidden layers of data and cluster (or group) data together based on similarities or differences.
For example, say your business wants to analyze data to identify customer segments. But you don’t know what segments exist yet. You’ll have to feed the unlabeled input data into the unsupervised learning model so it can act as its own classifier of customer segments.
A reinforcement learning method is a trial-and-error approach that allows a model to learn using feedback from its own actions. The computer receives “positive feedback” when it correctly understands or classifies data and “negative feedback” when it fails. By “rewarding” good behavior and “punishing” bad behavior, this learning method reinforces the former. (And it differentiates reinforcement learning from supervised learning, in which a data scientist simply confirms or corrects the model rather than rewarding or punishing it.)
Reinforcement learning is used to help machines master complex tasks that come with massive datasets, such as driving a car. Through lots of trial and error, the program learns how to make a series of decisions, which is necessary for many multi-step processes.
What is Deep Learning?
Deep Learning is a subset of machine learning or can be said as a special kind of machine learning. It works technically in the same way as machine learning does, but with different capabilities and approaches. It is inspired by the functionality of human brain cells, which are called neurons, and leads to the concept of artificial neural networks. It is also called a deep neural network or deep neural learning.
In deep learning, models use different layers to learn and discover insights from the data.
Some popular applications of deep learning are self-driving cars, language translation, natural language processing, etc.
Some popular deep-learning models are:
- Convolutional Neural Network
- Recurrent Neural Network
- Classic Neural Networks, etc.
How Deep Learning Works?
We can understand the working of deep learning with the same example of identifying cat vs. dog. The deep learning model takes the images as the input and feeds them directly to the algorithms without requiring any manual feature extraction step. The images pass to the different layers of the artificial neural network and predict the final output.
Examples of Deep learning
Here are just a few of the tasks that deep learning supports today and the list will just continue to grow as the algorithms continue to learn via the infusion of data.
- Virtual assistants
Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.
In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people, and those in government.
Vision for driverless delivery trucks, drones, and autonomous cars
The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street, or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.
- Chatbots and service bots
Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.
- Image colorization
Transforming black-and-white images into color was formerly a task done meticulously by the human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.
- Facial recognition
Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenge for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.
- Medicine and pharmaceuticals
From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.
- Personalized shopping and entertainment
Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.
Differences between Machine Learning and deep learning
This is a common question and if you have read this far, you probably know by now that it should not be asked in that way. Deep learning algorithms are Machine Learning algorithms. Therefore, it might be better to think about what makes deep learning special within the field of Machine Learning. The answer: the ANN algorithm structure, the lower need for human intervention, and the larger data requirements.
First and foremost, while traditional Machine Learning algorithms have a rather simple structure, such as linear regression or a decision tree, deep learning is based on an artificial neural network. This multi-layered ANN is, like a human brain, complex and intertwined.
Secondly, deep learning algorithms require much less human intervention. Remember the Tesla example? If the STOP sign image recognition was a more traditional machine learning algorithm, a software engineer would manually choose features and a classifier to sort images, check whether the output is as required, and adjust the algorithm if this is not the case. As a deep learning algorithm, however, the features are extracted automatically, and the algorithm learns from its own errors.
Thirdly, deep learning requires much more data than a traditional Machine Learning algorithm to function properly. Machine Learning works with a thousand data points, and deep learning oftentimes only with millions. Due to the complex multi-layer structure, a deep learning system needs a large dataset to eliminate fluctuations and make high-quality interpretations.
In conclusion, we can say that deep learning is machine learning with more capabilities and a different working approach. And selecting any of them to solve a particular problem is depend on the amount of data and complexity of the problem.