Machine Learning VS Deep Learning, What You Should Know
Machine learning VS deep learning is such a hot issue in the world of artificial intelligence. They are often used interchangeably and there are many misconceptions about them. However, they are not the same things. Both machine learning and deep learning have their own characteristics and functions.
They can be considered as two different categories of AI. Then, they are typically referred to as statistical modeling of data to make predictions or acquire meaningful information. Since they are both used everywhere, it is important to understand the difference between them instead of using the terms interchangeably.
In our everyday modern life, these two can be found in your favorite social media and movie streaming. It is how your movie streaming app shows you what to watch next. It is how your social media app knows whose face is on a picture. So, knowing machine learning VS deep learning is crucial.
There are some other things out there employing these two. In the world of business, this is how a customer service representative can know whether you are satisfied with their supports or not before you take a customer satisfaction survey. For further, let’s learn the differences between these two below!
Machine Learning VS Deep Learning, Overview
Let’s start with the definition of both machine learning and deep learning. ML is a way of statistical learning where each sample in a data collection is portrayed by a set of attributes and features. In contrary, deep learning refers to a method of statistical learning that takes out attributes or features from raw data.
When it comes to deep learning, it works by using neural networks with loads of concealed big data, layers, and strong computational resources. In some cases, the terms are somewhat similar. But, deep learning techniques develop representations of the data in an automated way. But, data representations are hard-coded in ML.
When we learn about machine learning VS deep learning, we tend to realize that these two are also different from AI algorithms. In ML and deep learning, we can find a variety of models. The models are categorized into two: supervised and unsupervised. They involve many models including k-Means and hierarchical clustering.
Supervised learning incorporates an output label related to each sample in the set of data. This output can be either rear-valued or categorical. Regression models, for instance, estimate real-valued outputs while classification models calculate discrete-valued outputs. Easy binary classification only has two labels of output.
Machine Learning, an Approach to Accomplish AI
To make it simpler, let’s see the details of these two terms one by one. Machine learning or ML is the practice of employing algorithms to parse dataset, learn from it, and create a prediction or determination about it. Instead of hand-coding software routines, the machine is trained by data and algorithms.
As you learn machine learning VS deep learning, it is important to know that one of the best applications for ML is computer vision. Even though it still calls for hand-coding, it makes a great application among the other use cases of ML. But, it doesn’t necessarily make ML rival humans.
A simple example of an ML algorithm is a music streaming service. To make it able to recommend new songs to a listener, ML algorithms related to the listener’s preferences are used. In this case, the recommendations are also given based on the other listeners who have the same taste.
Machine learning is now being used by multiple industries and running all sorts of automatic tasks. From finance professionals calling for favorable trades to data security enterprises hunting down malware, you can find the use of ML in diverse environments. They act as virtual personal assistants.
Deep Learning, a Technique for Applying ML
When we learn about machine learning VS deep learning, we shouldn’t miss how deep learning works. Different from machine learning, a deep learning model is crafted to constantly analyze data with a logic arrangement. It is similar to how we make conclusions. For this reason, deep learning utilizes ANN.
ANN stands for Artificial Neural Network, a design inspired by our biological neural system of the human brain. This is used for a smart machine which is far more powerful than a standard machine learning model. Once a deep learning model is working, a scientific marvel is born.
A great use case of deep learning is Google’s AlphaGO. This computer program can learn to play the abstract board game named Go. You need sharp intuition and intellect to play this game. Using its deep learning model, AlphaGo learns how to play at the next level.
Back to the comparison of machine learning VS deep learning, we can say that deep learning goes further than ML. For instance, image recognition by machine learning can be trained through deep learning to become better than humans. Deep learning is also used to recognize indicators for cancer.
Machine Learning VS Deep Learning, Conclusion
In the end, both machine learning and deep learning has the ability to handle immense dataset sizes. Nevertheless, ML methods will do better with a small set of data. For instance, when there are 100 data points, ML models like decision trees and k-nearest neighbors will make much more sense.
When we are talking about practical terms, deep learning seems like a subset of ML. It is like machine learning with different capabilities. While machine learning still requires hand-coded interfering, deep learning algorithms typically can determine everything themselves without requiring the engineer to make an adjustment.
Both of them have been used in a variety of industries and businesses as well. When it comes to interpretability, both of them receive lots of criticisms as well. But, it doesn’t make them bad. They are still employed to help create something easier for us. In conclusion, machine learning and deep learning are not the same things. Despite being part of AI, they are both different. They work in a different way and result in something dissimilar too. So, we shouldn’t use the terms interchangeably anymore. That’s all about m