A journey From Artificial Intelligence to Deep Machine Learning
Ever since the invention of the first computer – and perhaps even before – one of man’s dreams was to make machines that can do human work, but much better.
Due to human error, many driving, medical, and aviation accidents occur globally every year. Additionally, in mathematical calculations, analyzing and solving equations, a lot of human error may happen. But as you know, computers are much more reliable to do these.
So if we create machines with the ability to think, analyze, and make humans-like decisions, we can leave a lot of time-consuming mental work to them. Reducing labor costs is another advantage that encourages investors to spend heavily on AI projects, including machine learning and deep learning projects.
The technology and knowledge of making machines that have decision-making power is continuously evolving.
The exact definition of “artificial intelligence” agreed upon by all scientists has not yet been provided, and this is by no means surprising. Because the “intelligence” meaning, which is a more fundamental concept, has not been clearly defined. In fact, scientists still do not precisely know: What intelligence is.
But in general, artificial intelligence is the study of how computers can do things that humans are currently doing better. Most of the writings and articles related to artificial intelligence have defined it as “knowledge of cognition and design of intelligent agents.” An “intelligent agent” is a system that can increase its chances of success after analysis and understanding its surroundings.
Most of the early research of artificial intelligence focused on machine games as well as proving mathematical theorems with the help of computers.
At first, computers could only perform such activities using a massive amount of searching and discoveries for solutions and then choosing the best one. But with the advancement of this science, the man reached the point where he could design machines capable of learning beyond solving problems. This area of artificial intelligence is called “machine learning.”
Machine learning is a subset of artificial intelligence and deals with the design of machines that learn by using the examples and their own experiences.
These machines learn and operate without detailed planning and dictating every single action. In machine learning, instead of programming everything, the data is given to a general algorithm, and it is this algorithm builds its logic based on the data.
The algorithm is an example-based classification of different topics. The algorithm can classify the data into distinct groups. For example, separating emails into spam and non-spam is an example of using a machine learning algorithm that improves based on machine experience. Recognizing faces from other parts of the image, recognizing the sick person (classifying people into the sick and healthy group) are other examples of machine learning.
But artificial intelligence has not stopped there. Deep learning is a more advanced stage of machine learning.
Deep machine learning:
Deep learning is a subset of machine learning that is distinguished by its problem-solving method. Machine learning requires a domain specialist to identify more functional features. But in deep learning, a computer model learns to perform classification tasks directly from images, sound, or text. Therefore, in this method, the machine learns the features gradually. This makes deep learning algorithms take longer to train than machine learning, which only takes a few seconds or a few hours. However, during testing, the opposite is true. Deep learning algorithms take less time to perform tests than machine learning.
Most deep learning methods use a neural network architecture, which is why deep learning models are often called deep neural networks. The term “deep” refers to the hidden layers in the network. Traditional neural networks consist of only 2 to 3 hidden layers, while deep neural networks can have 150 layers.
Choose between machine learning and deep learning:
Machine learning offers various techniques and models that you can choose based on your schedule, processed data size, and project type. A successful deep learning project requires a large amount of data (for example, thousands of images) to model training as well as a GPU or graphics processing unit for fast data processing.
When deciding between machine learning and deep learning, consider if you have a high-performance GPU and labeled data. If you do not have any of these, machine learning can be used instead of deep learning. Deep learning is generally more complex, so you need at least a few thousand images to get reliable results. Having a high-performance GPU means that the model will have less time to analyze all those images.
Remember that using more advanced technology is not always the answer. Decide based on your preferences and what you want to do.