Deep Learning
1. What is Deep Learning?
Deep learning is an important component of data science, including statistical and predictive modeling. Machine learning algorithms used to collect, analyze, and interpret large amounts of data are 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 mimicking a human brain and the human brain is a collection of cells called neurons. To connect the neurons in the complex system our brain represents exist synapses, which play an important role in providing communication between the neurons. Synapses have a very simple way to change the signal by secreting certain hormones when they receive a signal from one neuron, and once the modified signal crosses a certain threshold, it is sent on to the next neuron. The systems 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 a human brain. Automatic speech recognition, image recognition, and natural language processing, which are functions of AI, can be used for autonomous vehicles, black and white image colorizing, face recognition, health condition classification and diagnosis, recommendation services, and more.
3. Deep Learning with CLICK AI
CLICK AI can use automated machine learning capabilities to accelerate your transition to being your very own AI engineer. The platform can help implement artificial intelligence with one click without time-consuming coding by preparing and preprocessing data, model development, and verification, resulting in high accuracy AI that can be applied in any given 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.
