Artificial intelligence is rather hard to define, if only because researchers themselves do not agree on what that concept should or should not include. But one thing is certain: when you hear about recent advances in artificial intelligence, more likely than not, it is related to the amazing advances made in one particular field of artificial intelligence, namely supervised learning. Imagine that you are a cardiologist and that you have to predict the risk of recurrence of a patient who has just had a heart problem. You will look at his sex, his age, his weight, his blood pressure, his lifestyle, his family history, etc., then you will make a prediction. You could ask a mathematical model to make that prediction on your behalf.
Hi guys are you working on neural networks or deep learning models and came across activation functions? And wondering what activation function is and why do we even need to use them in our deep learning models. In this post we are going to talk about activation function and why we should use activation functions in our neural networks. If you are planning to work in deep learning & build models for image, video or text recognition, You must have very good understanding of activation function and why they are required. I am going to cover this topic in a very non-mathematical and non-technical way so that you can relate to it and build an intuition.
"It smells of something a little funny coming in, but we can make due with that if that means a little bit nicer things," Keuchel cracked. "Not to say anything bad about Kissimmee, because Kissimmee was nice for 30-something years, but we're now in the 21st century with some of this nice stuff and a little bit better locker room and facility."
One of the major issues with artificial neural networks is that the models are quite complicated. For example, let's consider a neural network that's pulling data from an image from the MNIST database (28 by 28 pixels), feeds into two hidden layers with 30 neurons, and finally reaches a soft-max layer of 10 neurons. The total number of parameters in the network is nearly 25,000. This can be quite problematic, and to understand why, let's take a look at the example data in the figure below. Using the data, we train two different models - a linear model and a degree 12 polynomial.
SAS supports the creation of deep neural network models. Examples of these models include convolutional neural networks, recurrent neural networks, feedforward neural networks and autoencoder neural networks. Let's examine in more detail how SAS creates deep learning models using SAS Visual Data Mining and Machine Learning. SAS Visual Mining and Machine Learning takes advantage of SAS Cloud Analytic Services (CAS) to perform what are referred to as CAS actions. You use CAS actions to load data, transform data, compute statistics, perform analytics and create output.