Deep Learning

1. What is Deep Learning?

Deep learning is an important component of data science, including statistical and predictive modeling. Machine learning algorithms that need to collect, analyze, and interpret large amounts of data, stacked in a complex hierarchical structure. Deep learning programming can generate complex statistical models directly from its iterative outputs, thus generating accurate predictive models from large amounts of unlabeled, unstructured data.

Deep learning is a methodology created with the aim of "let's make a human brain," and the human brain is a collection of cells called neurons. Between neurons and neurons, there is something called a synapse, which plays an important role in connecting neurons and neurons. Synapse has a very simple way to change the signal by secreting chemicals when it receives a signal from a neuron, and when the changed signal crosses a certain threshold, it is sent to the next neuron. The ones created by imitating this method are Perceptron and Artificial Neural Network.

2. How is deep learning used?

Today, deep learning can be applied to a variety of fields because it has a way to solve complex problems without human intervention in a manner similar to that of the human brain. Automatic speech recognition, image recognition, and natural language processing, which are functions of AI, can be used for autonomous vehicles, coloring of black and white images, face recognition, classification and disease diagnosis, and recommendation services. 

3. Deep Learning with CLICK AI

CLICK AI can use automated machine learning capabilities to accelerate your transition to AI engineers. CLICK AI can implement artificial intelligence with one click without cumbersome coding by preparing and preprocessing data, modeling, modeling, and verification. Artificial intelligence with high accuracy can be introduced by selecting artificial intelligence specialized in industry. CLICK AI enables the implementation of artificial intelligence by providing multi-core high-performance graphics processing units (GPUs) and other similar processing units to increase efficiency and reduce time consumption.


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