Deep learning in today's world
The impact of deep learning is significant – and it’s only getting started.
Deep learning and GANs: How they relate
A generative adversarial network (GAN) is a type of machine learning algorithm. GANs help data scientists create synthetic data for data-hungry deep learning models. That’s important because using synthetic data allows for the creation of deep learning models not previously possible.
Discover how SAS uses deep learning
This step-by-step guide compares multiple neural network models and explains how to use them. You'll get an introduction to deep learning techniques and applications and learn how SAS supports the creation of deep neural network models.
How to use deep learning for embedding images
Embedding models reduce the dimensionality of input data, such as images. With an embedding model, input images are converted into low-dimensional vectors – so it's easier for other computer vision tasks to use. The key is to train the model so similar images are converted to similar vectors.
How deep learning works
Deep learning changes how you think about representing the problems that you’re solving with analytics. It moves from telling the computer how to solve a problem to training the computer to solve the problem itself.
Deep learning is a paradigm shift in model building that moves from feature engineering to feature representation.
Deep learning layers
Instead of using known variables to predict unknowns, deep learning uses looks at layers of the data to recognise latent features of the data.
Deep learning results
The promise of deep learning is that it can lead to predictive systems that generalise well, adapt well, continuously improve as new data arrives. You no longer fit a model. Instead, you train the task.
A traditional approach to analytics is to use the data at hand to engineer features to derive new variables, then select an analytic model and finally estimate the parameters (or the unknowns) of that model. These techniques can yield predictive systems that do not generalise well because completeness and correctness depend on the quality of the model and its features. For example, if you develop a fraud model with feature engineering, you start with a set of variables, and you most likely derive a model from those variables using data transformations. You may end up with 30,000 variables that your model depends on, then you have to shape the model, figure out which variables are meaningful, which ones are not, and so on. Adding more data requires you to do it all over again.
The new approach with deep learning is to replace the formulation and specification of the model with hierarchical characterisations (or layers) that learn to recognise latent features of the data from the regularities in the layers.
Advanced Analytics from SAS
Deep learning is just one technique in the data scientist's toolkit. Learn about other advanced analytics techniques, including forecasting, text analytics and optimisation.
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