The Future of Computer Vision: Harnessing the Power of Deep Learning with SAS

Computer vision has come a long way in recent years, thanks to advancements in deep learning algorithms and frameworks. One of the leading platforms in this field is SAS, which offers a comprehensive suite of tools and capabilities for developing and deploying deep learning models specifically tailored for computer vision tasks.

What is Deep Learning?

Before diving into the specifics of deep learning for computer vision with SAS, let's first understand what deep learning is. Deep learning is a subset of machine learning that utilizes artificial neural networks (ANNs) to process and analyze vast amounts of complex data. It is inspired by the human brain's structure and function, allowing the system to learn and make decisions on its own.

The Importance of Computer Vision

Computer vision is a field of research that focuses on enabling computers to interpret and understand visual information, just like humans do. With the prevalence of digital images and videos in today's world, computer vision has become a critical technology with numerous applications.

Deep Learning for Computer Vision with SAS: An Introduction

by Robert Blanchard (Kindle Edition)

4.7 out of 5

Language	:	English
File size	:	8249 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	150 pages
Screen Reader	:	Supported

FREE

From self-driving cars to facial recognition systems, computer vision is revolutionizing various industries. It allows machines to analyze and make sense of visual data in real-time, enabling automation, object detection, image classification, and much more.

That's where deep learning comes into play. Deep learning algorithms excel at handling large and complex visual datasets, making them ideal for computer vision tasks. When combined with the powerful capabilities of SAS, the possibilities for innovation become endless.

SAS: A Leader in Deep Learning for Computer Vision

SAS, a renowned software company specializing in analytics and data management, offers a comprehensive deep learning framework called SAS® Viya®. This platform provides a wealth of tools and functionalities to develop, train, and deploy deep learning models for computer vision applications with ease and efficiency.

SAS Viya combines the power of deep neural networks, computer vision libraries, and scalable computing resources to deliver state-of-the-art results. Built on a scalable and distributed architecture, SAS Viya allows users to leverage parallel processing and GPU acceleration to train models faster.

Key Features and Capabilities of SAS Viya for Computer Vision

1. Pre-trained Models:

SAS Viya comes with a wide range of pre-trained models, allowing users to quickly get started with computer vision projects. These models have been trained on massive datasets, making them highly accurate and reliable for various image analysis tasks.

2. Transfer Learning:

Transfer learning is a technique that enables the transfer of knowledge learned from one task to another. SAS Viya supports transfer learning, allowing users to fine-tune pre-trained models for specific computer vision applications without starting from scratch. This significantly reduces the time and effort required to develop custom models.

3. GPU Acceleration:

Utilizing GPUs (Graphics Processing Units) can significantly speed up the training process of deep learning models. SAS Viya supports GPU acceleration, enabling users to leverage the power of parallel processing to train models faster and achieve real-time results.

4. Model Deployment and Integration:

SAS Viya provides seamless capabilities to deploy and integrate deep learning models into existing systems and applications. Users can easily deploy models on-premises, on cloud platforms, or in hybrid environments, enabling the utilization of models in real-world scenarios efficiently.

Use Cases of Deep Learning for Computer Vision with SAS

The combination of deep learning and SAS Viya opens up a wide range of applications for computer vision. Here are a few examples of how this powerful combination can be used:

1. Object Detection and Recognition:

Deep learning models can be trained to detect and recognize objects in images or videos with great accuracy. This is particularly useful in autonomous driving systems, surveillance, and security applications.

2. Image Classification:

SAS Viya enables the development of deep learning models for image classification tasks. This can be utilized in medical imaging, quality control in manufacturing, and various other scenarios where accurate classification of images is required.

3. Facial Recognition and Emotion Analysis:

With deep learning models, SAS Viya allows the identification and analysis of faces in images or videos. This can be useful in fields such as law enforcement, customer sentiment analysis, and personalized marketing.

Deep learning for computer vision with SAS Viya is paving the way for groundbreaking advancements in various industries. The combination of SAS' powerful capabilities and the ever-evolving deep learning algorithms empowers developers and researchers to create robust and efficient computer vision systems.

As technology continues to progress, we can expect computer vision to play an increasingly vital role in our daily lives. Thanks to SAS and the power of deep learning, the future of computer vision holds countless possibilities and exciting innovations that were once unimaginable.

Deep Learning for Computer Vision with SAS: An Introduction

by Robert Blanchard (Kindle Edition)

4.7 out of 5

Language	:	English
File size	:	8249 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	150 pages
Screen Reader	:	Supported

FREE

Discover deep learning and computer vision with SAS!

Deep Learning for Computer Vision with SAS^®: An introduces the pivotal components of deep learning. Readers will gain an in-depth understanding of how to build deep feedforward and convolutional neural networks, as well as variants of denoising autoencoders. Transfer learning is covered to help readers learn about this emerging field. Containing a mix of theory and application, this book will also briefly cover methods for customizing deep learning models to solve novel business problems or answer research questions. SAS programs and data are included to reinforce key concepts and allow readers to follow along with included demonstrations.

Readers will learn how to:

• Define and understand deep learning
  
• Build models using deep learning techniques and SAS Viya
  
• Apply models to score (inference) new data
  
• Modify data for better analysis results
  
• Search the hyperparameter space of a deep learning model
  
• Leverage transfer learning using supervised and unsupervised methods