What is a dynamic neural network?

Dynamic neural network is an emerging research topic in deep learning. The important research problems of dynamic networks, e.g., architecture design, decision making scheme, optimization technique and applications, are reviewed systematically.

Which algorithm is used in CNN?

convolutional neural network
The most established algorithm among various deep learning models is convolutional neural network (CNN), a class of artificial neural networks that has been a dominant method in computer vision tasks since the astonishing results were shared on the object recognition competition known as the ImageNet Large Scale Visual …

What are examples of dynamic networks in deep learning?

Dynamic Neural Networks: An Example For example, convolutional neural networks (CNNs), which apply fixed-structured operations to fixed-sized images (Figure 1), are highly effective precisely because they capture the spatial invariance common in computer vision domains.

What is RNN algorithm?

Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple’s Siri and and Google’s voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data.

How are dynamic neural networks trained in deep learning?

Dynamic networks are trained in the Deep Learning Toolbox software using the same gradient-based algorithms that were described in Multilayer Shallow Neural Networks and Backpropagation Training. You can select from any of the training functions that were presented in that topic. Examples are provided in the following sections.

Which is an example of a dynamic neural network?

One principal application of dynamic neural networks is in control systems. This application is discussed in detail in Neural Network Control Systems. Dynamic networks are also well suited for filtering.

How are feedforward and recurrent neural networks work?

How Dynamic Neural Networks Work 1 Feedforward and Recurrent Neural Networks. Dynamic networks can be divided into two categories: those that have only feedforward connections, and those that have feedback, or recurrent, connections. 2 Applications of Dynamic Networks. 3 Dynamic Network Structures. 4 Dynamic Network Training.

How are gradient descent algorithms used in neural networks?

Optimizers for neural networks typically use some form of gradient descent algorithm to drive the backpropagation, often with a mechanism to help avoid becoming stuck in local minima, such as optimizing randomly selected mini-batches (Stochastic Gradient Descent) and applying momentum corrections to the gradient.