Tag: Deep Learning

Feed: Featured Blog Posts - Data Science Central. Author: ajit jaokar. In this post, we examine applications of deep learning to three key biomedical problems:   patient classification, fundamental biological processes, and treatment of patients. The objective is to predict whether deep learning will transform these tasks. The post is based on the very comprehensive paper “Opportunities and obstacles for deep learning in biology and medicine”. The paper places a high bar i.e. on the lines of Andy Grove’s inflection point to refer to a change in technologies or environment that requires a business to be fundamentally reshaped. The three classes ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Roger Max. Transcending human perception, today autonomous vehicles or AVs have become a reality. Auto giants such as Tesla, BMW, Google, Volkswagen and Volvo have been the front runners in introducing autonomous transportation to ease the pressure off. Meanwhile, the market growth for AV or autonomous vehicles is estimated to touch 40% of CAGR, annually in the next six years. Before going further, it would be worth knowing how vehicles can function autonomously. An AV or autonomous vehicle is equipped with a vision system, LIDAR or radar based sensing and multiple ... Read More

Feed: AWS Architecture Blog. You want to start quickly when doing deep learning using GPU-activated Elastic Compute Cloud (Amazon EC2) instances in the AWS Cloud. Although AWS provides end-to-end machine learning (ML) in Amazon SageMaker, working at the deep learning frameworks level, the quickest way to start is with AWS Deep Learning AMIs (DLAMIs), which provide preconfigured Conda environments for most of the popular frameworks. DLAMIs make it straightforward to launch Amazon EC2 instances, but these instances do not automatically provide the high-performance graphics visualization required during deep learning research. Additionally, they are not preconfigured to use AWS storage services or ... Read More

Feed: Recent Announcements. Today, we are excited to announce AWS Deep Learning Containers (DLCs) with integrated SDKs for inference that enable customers to easily deploy Hugging Face models in Amazon SageMaker at scale. This was the number one requirement from customers that have been leveraging the Hugging Face AWS DLCs for training (Hugging Face training DLCs) that released in March 2021. Starting today, Amazon SageMaker supports deploying Hugging Face models using the Hugging Face AWS Deep Learning Containers for inference (Hugging Face inference DLCs) in addition to the Hugging Face training DLCs. The Hugging Face inference DLC supports both TensorFlow ... Read More

Feed: Recent Announcements. We are excited to announce that AWS now offers new framework-specific Deep Learning AMI (DLAMI). Today we launched framework-specific DLAMIs for PyTorch 1.9.0 and TensorFlow 2.5.0, with support for Amazon Linux 2, Ubuntu 18.04, and Ubuntu 20.04. The AWS DLAMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. These optimized new images are up to 60% smaller in size, accelerating the time for machine learning practitioners to launch DLAMIs on AWS. We will continue to release and support DLAMI in the previous format that ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Oleksandr Tyron. Recommender systems are lifesavers in the infinite seething sea of e-commerce, improving customer experience. Recommender engines are eliminating the tyranny of choice, smoothing the way for decision-making, and boosting online sales. Moreover, ubiquitous AI technologies are sneaking into e-commerce, too, not only solving the problems of irrelevant recommendations but predicting the customer’s next steps.  In light of the pandemic’s impact, online sales are projected to grow twice in the next three years. Under those circumstances, businesses should ensure a perfect customer experience with precise recommendations to stand out. Read ... Read More

Feed: Neo4j Graph Database Platform. Author: Elaine Rosenberg. Hello, everyone! We at Neo4j are working to continually improve the UX experience for our users. If you have the time, please consider meeting with us to discuss your experience with our products and what you would like to see in future releases. In this week’s episode, Neo4j’s David Allen shares his experience around batching vs. streaming data into Neo4j. Neo4j’s Alex Woolford and Rob Martin developed a quick and easy example for demonstrating how to move data from Kafka to Neo4j, and Clair Sullivan posted a blog that describes how easy ... Read More

Feed: R-bloggers. Author: Learning Machines. This is our 101’st blog post here on Learning Machines and we have prepared something very special for you! Oftentimes the different concepts of data science, namely artificial intelligence (AI), machine learning (ML), and deep learning (DL) are confused… so we asked the most advanced AI in the world, OpenAI GPT-3, to write a guest post for us to provide some clarification on their definitions and how they are related. We are most delighted to present this very impressive (and only slightly redacted) essay to you – enjoy! Artificial intelligence (AI), machine learning (ML), and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Zhai CreativoMedia. How Deep Learning is Changing the Future of SEO Algorithms have become smarter. They’re learning what websites you frequent, why you typed a certain query and what other search suggestions you’re likely to click. Behind the online results you’re seeing is a highly advanced form of AI: deep learning. It’s based on artificial neural networks that allow the machine to learn without human supervision. Deep learning requires more data because neural networks need to process more information to be more efficient, to gain better insights — to better answer ... Read More

Feed: SAS Blogs. Author: Gül Ege. Note from Gül Ege Sr. Director, Analytics R&D, IoT: The pattern of training in the Cloud, with your choices of framework and inferencing at the Edge with a target environment, are especially common in Internet of Things (IoT). In IoT, there is a proliferation of hardware environments on the Edge. SAS and Microsoft are collaborating around ONNX Runtime. The goal is for developers and systems integrators to have the deployment flexibility they need to train deep learning models using a variety of frameworks. As well, they can seamlessly deploy on a variety of hardware ... Read More