Deep learning refers to a form of machine learning that uses neural networks modeled after the human brain. By making the switch to deep learning-based machine learning, the past few years have seen a rapid improvement in image and voice recognition technology, even outperforming humans in certain areas. Compared to conventional forms of machine learning, deep learning is especially notable for its high versatility, with applications in a wide variety of fields besides image and voice recognition, including machine translation, signal processing, and robotics. As proposals are made to expand the scope of deep learning to fields where machine learning has not been traditionally used, there has been an accompanying surge in the number of deep learning developers.
The work of neural network design is very important for deep learning program development. Programmers construct the neural network best suited to the task at hand, such as image or voice recognition, and load it into a product or service after optimizing the network's performance through a series of trials.
Currently, when creating conventional deep learning programs, neural networks are constructed by writing the program code and combining function blocks. However, by using this newly developed console software, this function block concept can be simply expressed through the GUI. On the console software screen, the different layers (function blocks) are prepared beforehand in the shape of ready-made components. Neural networks can then be constructed by simply rearranging these components through the GUI, greatly improving the efficiency of program development. Moreover, novice deep learning developers can visually confirm the functions of the core library and build up their proficiency in a short amount of time.