Artificial Neural Networks Report

We also provide guidance on how to choose the learning rate. If we choose learning speed too high, it may be on the "right" direction moving too far, leading to overshoot of the wrong time or minimum training surface (in this case for accuracy It can be influenced and take time It is very bad, lowering the learning speed improves the speed and accuracy of training, but too slow learning speed wastes time and computational resources The method is explained as follows: To train the neural network for a period of time, by using different levels of learning (eg 100 to 0.00001) it is possible to find the one with the smallest learning rate L it can

This paper describes an artificial neural network. Describe and demonstrate various types of neural networks, describe the application of neural networks such as artificial neural networks to medicine, and provide a detailed historical background. The relationship between artifacts and artifacts has also been studied and explained. Finally, introduce and demonstrate the mathematical model involved. An artificial neural network (ANN) is an example of information processing inspired by a biological nervous system such as the brain processing information. An important element of this paradigm is the new structure of the information processing system. It consists of a number of highly interconnected processing elements (neurons) working together to solve specific problems. An artificial neural network is like a person and learns through examples. Set up ANN for specific applications through learning processes such as pattern recognition and data classification

Reports made based on depth are defined as AI artificial neural network learning techniques such as feed forward neural network, recurrent neural network (RNN) and convolution neural network (CNN). These algorithms have evolved from exactly initiated research courses to mature technologies used in the real world. This report does not cover advanced artificial intelligence technologies such as generation-confrontation-network (GAN) and reinforcement learning. Of the 19 industries surveyed, the potential annual value of AI is 3.5 trillion to 5.8 trillion dollars. The AI ​​retail industry is expected to receive the maximum impact of $ 0.4 to 0.8 billion in tourism (US $ 0.3-0.5 billion), as well as transportation / logistics (US $ 0.4 to 0 500 million). Marketing and sales and supply chain management and manufacturing are areas that help AI increase corporate annual revenue by 1.2 to 2.6 trillion dollars.

In this course, we will learn the intuition, the artificial neural network actually used, behind the artificial neural network, it will understand the intuition behind the convolution neural network, in fact the neural network conc Vortar returns back to understanding the neural network Intuitively, in practical application recurrent neural networks, it will understand the self-organizing map intuitively behind, self-organization of practice Applied to the map, but it actually understands the intuition behind AutoEncoders who will understand the intuitive Boltzmann machine behind the Boltzmann machine