Introduction beginners reading interpretation of 14 key words – deep learning technology Sohu ekdv-273

| entry beginners reading: Interpretation of 14 key words – deep learning technology from Sohu KDnuggets Author: Matthew Mayo machine: Xuwen Wang, compiled in the heart of Chen Chen is introduced in this paper including LSTM, ANNS, biological neurons, back propagation, multiple perceptron learning depth 14 key concepts, for beginners to understand these key words are important to the understanding of deep learning. The heart of the machine has been introduced in a September article on other terms of deep learning. Although the amount of search in the recent online search has been high, deep learning is still a relatively new concept. Because of the great success in various fields, machine learning has emerged in the field of research and production. Machine learning is a process of applying deep neural network technology — that is, the neural network architecture with multiple hidden layers to solve the problem. Like data mining, deep learning is also a process that uses a neural network architecture – a specific machine learning algorithm. In recent years, deep learning has accumulated considerable research results. Therefore, in my opinion, the following points in mind of machine learning is very important: as shown above, deep learning deep learning in data mining, like (depth) neural network in machine learning (process VS architecture). At the same time, we can also see the depth of the neural network to a large extent belongs to the current situation of artificial intelligence. The two concepts are intertwined almost to the same extent (but actually they are not the same thing, artificial intelligence neural network but also contains a large number of other algorithms and techniques) at the same time, learning process and neural network technology to lead in depth, in recent years in related fields has made great leaps and bounds. Among them, the relationship between deep learning / deep neural networks and computer vision, Natural Language Processing, and generative models is of concern. Therefore, let us through the definition of concise and to the point to understand, deep learning and related terms. 1 deep learning, as defined above, is a process of applying neural networks to solve problems. The depth neural network is a neural network with at least one hidden layer (see figure below). Like data mining, deep learning refers to a specific process. Which uses the depth neural network – a specific framework for machine learning algorithms. 2 artificial neural network (ANNs) machine learning framework is the earliest inspiration from the biological brain (especially neurons) depth learning to apply the concept of neurons. In fact, a single artificial neural network (not a deep neural network) was discovered long ago and has been able to solve certain problems in the past. However, compared to the present, the neural network architecture is designed to contain several hidden layers (except for simple input and output layers). Increasing the number of layers increases the complexity of the network, making the network able to carry out deep learning and become a more powerful problem solving tool. In fact, people n相关的主题文章: