As the technology evolves, so does the vocabulary. There are many new terms, whose meaning is not quite clear. Machine learning and deep learning are good examples of names that are often used interchangeably but do not exactly mean the same thing.
Artificial intelligence covers all the issues related to simulating human intelligence by machines, such as reasoning, recognition, problem solving or learning. This term is used very widely and may refer to anything from chess programs, voice recognition systems to self-driving cars and many other possible applications.
Wide use of AI also means a variety of approaches to the development and studies of the subject. You can’t define one consistent theory or paradigm of AI research. Many emerging questions and problems make the researchers argue about how AI should evolve. Creating a self-learning machines is one of the proposed solutions.
Machine learning (ML) should be considered a subfield of AI. The basic principle here is that machine is given set of training examples, and on that basis learns how to recognize patterns and assign data to specific labels. ML algorithms can be applied to many tasks such as object recognition, translations or fraud detection, although it requires specific instructions to perform a task correctly.
Deep learning (DL) is a specific class of machine learning. It uses multiple layers of neural networks to process and analyze massive datasets. Each layer is focused on more complex and abstract aspects of given data. It also uses previous layers output as an input, so each level of analysis gives more accurate results. DL requires huge amounts of data to be able to learn how to spot minor differences and assign output to different classes correctly. Its ability to independently detect patterns and predictions gives it an edge over hand-coded programs.
The efficiency of processing vast databases is one of the differences between machine learning and deep learning. ML performs well on smaller input. DL algorithms, on the other hand, shows its full potential on huge amounts of data, because they can use it to learn and improve its abilities. Deep learning also requires more computing power due to its complex, multi-level nature. Execution time is another important factor. DL requires a lot of time to learn, because of the variety of processed parameters.
DL and ML also differ in terms of approach to problem solving. Traditional machine learning tends to divide problem into separate parts, solve each of them and combine the results. Deep learning is inclined towards considering the task as a whole.
The biggest difference can be seen in process of creating feature extractors. To build specific patterns, system requires to learn features necessary for correct classification. In typical machine learning these features need to be determined by an expert and coded manually.
Deep learning algorithms are programmed to learn features from data independently. This is a significant step ahead of traditional machine learning. Deep learning eliminates the need for manual coding of feature extractors. What’s more, the results are more accurate and processed faster.
Machine learning can be considered a groundwork for deep learning. DL is based on similar concept, but its ability to teach itself and adjust to new problems gives it significant advantage over traditional ML model.
While there are many benefits to working remotely, it also brings new challenges. Syncing up…