This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, and recurrent networks. Neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Lastly, transfer learning is covered because the emergence of this field has shown promise in deep learning.
Learn How To
Define and understand deep learning.
Build traditional, convolutional, and recurrent neural networks using deep learning techniques.
Apply models to score new data.
Search the hyperparameter space of a deep learning model.
Leverage transfer learning using supervised and unsupervised methods.
Who Should Attend
Machine learners and those interested in deep learning, computer vision, or natural language processing
Before attending this course, you should have at least an introductory-level familiarity with basic neural network modeling and basic machine learning. You can gain this experience by completing the cpml course or the Neural Networks: Essentials course. Previous SAS software experience is helpful but not required.
SAS Products Covered
SAS Viya;SAS Visual Data Mining and Machine Learning
Introduction to Deep Learning
Introduction to neural networks.
Introduction to deep learning.
Convolutional Neural Networks
Introduction to convolutional neural networks.
Structure of a convolutional neural network.
Building and training a convolutional neural network.