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How to get started quickly in deep learning
In recent years, with the breakthrough of deep learning and cloud computing theory, major problems in speech recognition, image recognition, natural language processing and other fields have been broken one by one, and related papers and products have sprung up.

As we know, deep learning is a form of machine learning, which enables computers to learn from experience and understand the world from the hierarchical structure of concepts. Because computers collect knowledge from experience, human computer operators do not need to formally specify all the knowledge that computers need, that is, the machine itself has learning ability.

How to quickly start deep learning?

First of all, we need to master mathematical concepts, including linear algebra, probability theory and information theory, numerical calculation and related concepts in machine learning.

Then we need to learn the commonly used deep learning techniques, including deep feedforward network, regularization, optimization algorithm, convolution network, sequence modeling and practical methodology, as well as natural language processing, speech recognition, computer vision, online recommendation system and other applications.

Finally, we must practice more projects, especially those aimed at different industry application scenarios. Through the actual combat of these application scenarios, the understanding of deep learning algorithms is enhanced, and the business familiarity and code actual combat ability are improved.

In order to help industry talents master artificial intelligence technology faster, Zhonggong hired experts from Institute of Automation, Chinese Academy of Sciences to open the course of artificial intelligence "deep learning", and through in-depth analysis of deep learning technology in the field of artificial intelligence, cultivated core talents of artificial intelligence.

During the five-week course, you will fully understand the relevant knowledge of AI deep learning, master the principles of artificial neural network, convolutional neural network, circular neural network, generating countermeasure network and distributed processing of deep learning, and apply them to enterprise-level projects.

By mastering professional knowledge, you will have a more systematic understanding of the cutting-edge technologies of deep learning and have your own ideas on the development of the cutting-edge mainstream directions such as learning to learn (meta-learning) and transfer learning.