When people hear about deep learning and machine learning, many assume they’re the same. Although both belong to the artificial intelligence (AI) field, the truth is that they aren’t as interchangeable as people might think. This blog looks at deep learning vs. machine learning, how they work, and how Coding Dojo can help you break into tech.
What Is Machine Learning?
Machine learning is a branch of the artificial intelligence field which focuses on giving machines human-like problem-solving capabilities. Machine learning algorithms can uncover significant insights to make highly accurate predictions to help streamline the decision-making process.
Machine learning is unique because it can accurately imitate how the human brain learns and gradually improves over time by combining data sets and algorithms. As time goes on, everything from self-driving cars to the recommendations you see on Netflix directly results from machine learning. It should be interesting to see what the future of machine learning has in store.
How Does Machine Learning Work?
How machine learning works is the first step. It begins with inputting a piece of training data into a selected algorithm, similar to a specific request. The machine learning algorithm uses the second set of data to double-check that the first set of results was correct. If the results do not match, the algorithm is ‘re-trained’ several times until the data scientists get an accurate reading.
It’s important to point out that when it comes to types of machine learning, there are two kinds of data sets: labeled or unlabeled data. The difference between the two kinds is that labeled data has one or more identifying characteristics, mainly used in supervised machine learning. In contrast, unlabeled data has one or no identifying characteristics and doesn’t need any human assistance.
Types of Machine Learning
1. Supervised Learning
Supervised learning works because the machine learning algorithm uses a small training data set to analyze. This data set is a portion of a more extensive collection and gives the algorithm a starting point regarding what to evaluate. As the simplest type of machine learning, supervised learning works off of labeled data and requires human oversight, which can be a potent tool for data scientists.
The algorithm works on finding relationships between these characteristics and then gives a cause-and-effect prediction between each of the variables in the data set. The result is that the algorithm understands how the data works and the relationship between the input and output points.
2. Unsupervised Learning
Unsupervised learning has a clear advantage, mainly because it works with unlabeled data which needs no human assistance. Unsupervised learning works because the algorithm attempts to find the exact relationship between two data points. As unlabeled data needs only to identify one or no characteristics, there’s plenty of room to uncover hidden data patterns, making it highly versatile.
3. Reinforcement Learning
The inspiration for reinforcement learning comes from how human beings assess and understand data within their daily lives. The main difference with this type of machine learning is that the algorithm uses a trial-and-error method with the help of an ‘interpreter,’ consistently trying new approaches and making improvements along the way, favoring or reinforcing the positive results.
What Is Deep Learning?
Deep learning is a specific subfield of machine learning which uses a three-layer neural network. This network works by trying to act and function like the human brain. These neural networks attempt to mimic the behavior of the human brain by enabling it to learn from large amounts of data to make highly accurate predictions.
The three-layer neural network, which acts as a superhighway for sending and receiving information, is divided into input, hidden, and output layers, all of which are critical components for decision-making. Without human supervision, deep learning algorithms can do anything from fighting fraud to giving us hands-free TV remotes and even self-driving cars.
How Does Deep Learning Work?
Deep learning works by harnessing the power of its neural networks, which consist of multiple input or output layers that refine and optimize predictions, known as propagation. These input layers are responsible for intaking and interpreting the data sets, while the output layer spits out a final prediction.
If an error appears, a process known as backpropagation occurs. These deep learning algorithms recalculate the error and adjust the weights or biases. In some cases, forward propagation is used to fix glitches but only works going forward.
Types of Deep Learning
1. Recurrent Neural Networks
Recurrent neural networks (RNNs) analyze natural language and speech recognition. This type of deep learning works on finding data sequences or time series, which are data points over a specific period.
2. Convolutional Neural Networks
Convolutional neural networks (CNNs) are mainly for image classification and computer vision, a field of study in AI where computers analyze and categorize images. The main objective of CNNs is to search images for explicit features and patterns for object detection and recognition.
Deep Learning vs. Machine Learning: What’s the Difference?
1. Human Intervention
Machine learning algorithms usually need consistent human supervision to make their predictions. Deep learning algorithms are more complex and only require occasional human oversight depending on the task.
2. Time
On the topic of time, machine learning operates much faster but is less powerful when providing predictions. On the other hand, deep learning takes more time to process data but is far more intelligent and can continuously optimize itself.
3. Approach
While deep learning uses its neural networks to analyze large unorganized data sets, machine learning needs more structure when looking at data and specific algorithms for accurate results.
4. Hardware
The hardware that machine learning uses is usually simpler algorithms and can often run on traditional computers. In contrast, deep learning uses graphic processing units (GPUs) with ample memory storage and can hide delays in its memory transfer processes, making the system run more efficiently.
5. Applications
Whether it comes to filtering out junk mail from an email inbox or the security of an average banking system, both use machine learning algorithms. Deep learning is for far more complex tasks, such as self-driving cars or even robots that can perform life-saving surgeries.
Examples of Machine Learning Applications
- Speech Recognition
One of the most popular machine learning applications is speech recognition, also known as search by voice. Whether using Google, Siri, Alexa, or Cortana, speech recognition makes it possible to use any device without lifting a finger.
- Image Recognition
One of the most common uses for machine learning is image recognition – identifying various objects, people, places, or anything else. A great example of this is the automatic friend tagging option that platforms like Facebook offer using face recognition technology known as “deep face.”
- Traffic Prediction
Everyone knows that Google Maps is the number one app for getting help with directions or finding alternate routes around traffic jams. By using a combination of other users currently using the app, historical data patterns, and some trade tricks, anyone can avoid getting stuck in rush hour traffic.
- Language Translation
Google Translate, which runs off of Google Neural Machine Translation, is machine learning at its finest. Anyone with a cellphone can easily translate thousands of languages quickly, from single words to entire sentences.
- Product Recommendations
When it comes to product recommendations, most people’s minds go right to Amazon. As a buyer, it’s common to check out an item, and then some additional suggestions of other products appear after that. Considering that 35% of Amazon’s revenue comes from product recommendations, it’s not surprising that customers can feel bombarded.
Examples of Deep Learning Applications
- Healthcare
The possibilities of deep learning in healthcare are endless, starting with computer-assisted disease detection and diagnosis. Areas of medical research, new drug studies, and life-saving treatments from cancer to diabetes all become a reality with the help of deep learning.
- Fraud Detection
Deep learning can make it possible for areas with serious fraud issues, such as online banking, by digitally tracking all money transactions. Some examples, such as Autoencoders in Keras and Tensorflow, are in the development process, with the potential for helping to save billions in stolen funds. By looking at patterns of customer purchases and analyzing historical behavior, we’ll all be in safe hands.
- Virtual Assistants
Cloud-based virtual assistants will one day be able to understand different voice commands and seamlessly complete tasks for users. Present examples are Amazon Alexa, Cortana, Siri, and Google Assistant, which many people use every day and will one day become complete virtual assistants.
- Self-Driving Cars
When it comes to the future of self-driving cars, deep learning is the key to bringing this to life. By using deep learning, some cameras, and sensors, these autonomous vehicles will be able to navigate around traffic jams easily, find the shortest route, and watch out for pedestrians or other cars on the road.
- Natural Language Processing (NLP)
Natural Language Processing (NLP) is deep learning that can read, understand, and communicate human languages. With NLP, machines could quickly summarize documents, analyze text, and communicate just as well as any human.
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