Scaling a data set is a process of processing the data in advance of network training, limiting the range of data in the data set to ensure that they are not distributed over a wide range. Generally, scaling input data sets can improve the performance of neural networks and is one of the commonly used data preprocessing methods.

I hope we can understand some programming ideas and models of AI and help sort out the path of self-growth. Combing the overall knowledge system of artificial intelligence. I hope we can understand some programming ideas and models of AI and help sort out the path of self-growth. At present, the research fields of deep learning mainly include the following three groups. Scholars. I mainly do theoretical research on deep learning, studying how to design a "network model", how to...

Face detection is the first step of face recognition and processing, mainly used to detect and locate the face in the picture, return high-precision face frame coordinates and face feature point coordinates. Face recognition will further extract the identity features contained in each face, and compare it with the known face, so as to identify the identity of each face. At present, the application scene of face detection/recognition gradually evolved from indoor to outdoor, from a single limited scene...

This article is intended to help those who want to gain deep learning practice experience through the TensorFlow Eager pattern. TensorFlow Eager allows you to build neural networks as easily as you can with Numpy, with the great advantage of automatic differentiation (no handwritten back propagation, (*^▽^*)!). . It can also run on gpus to make nerves...

Last year, we launched Transformer, a new machine learning model that outperforms existing machine translation algorithms and other language understanding tasks. Prior to Transformer, most neural network-based machine translation methods relied on recursive neural networks (RNN) of cyclic operations, which used loops (i.e., each step's output went to the next step) to recursively...

How close are we to solving 2D&3D face alignment? Abstract: This paper investigates the degree to which a very deep neural network achieves near-saturation performance on existing 2D and 3D face alignment datasets. To this end, I...

In the training process of neural network, batch size is an important hyperparameter. Choosing an appropriate batch size can guarantee the generalization ability of the model and make the convergence more stable. In this section, we will examine the effect of changing batch size on accuracy.

How to use deep learning to break sudoku? What is RRN? Take a look at this article and learn about it. What is relational reasoning? Think of the picture above, don't think of this as a sphere, a cube, etc. We can think about it in terms of the millions of numbers that make up the pixel values of the image or the angles of all the edges of the image or we can think about each 10x10 pixel region. Try answering the following question: "Large sphere left...

https://mp.weixin.qq.com/s/ftkXl-45gKWY3DfjQvKywQ, you are A computer, your name is A long, long before you are not connected to any other computer, alone. Until one day, you want to be with another

Where $t_L $represents the sample number of layer L, and n represents the total node number. $W^{(l)}$= W^{(l)}$= W^{(l)}$= W^{(l)}$ That is, the node is sampled with probability proportional to the 2 norm of the column in $\hat A$matrix. The calculation is simple and the distribution is layer independent. But the authors fail to prove this estimate...