Category: Compute

A key barrier to the wider adoption of deep neural networks on industrial-size datasets is the time and resources required to train them. AlexNet, which won the 2012 ImageNet Large Scale Visual Recognition Competition (ILSVRC) and kicked off the current boom in deep neural networks, took nearly a week to train across the 1.2-million-image, 1000-category […]

Vehicles with self-driving technology can bring many benefits to society. One of the top priorities at Toyota Research Institute (TRI) is to apply the latest advancements in artificial intelligence (AI) to help Toyota produce cars that are safer, more accessible, and more environmentally friendly. To help TRI achieve their goals, they turned to deep learning […]

In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging Amazon Comprehend, paired with Amazon Aurora-MySQL and AWS Lambda.

This is a guest blog post by Ned T. Sahin, PhD (Brain Power LLC and Harvard University), Runpeng Liu (Brain Power LLC and MIT), Joseph Salisbury, PhD (Brain Power LLC), and Lillian Bu (Brain Power LLC and MIT).  Producers of content (from ads to games to teaching materials) usually judge the success of their content […]

Machine learning (ML) methods for the field of computational chemistry are growing at an accelerated rate. Easy access to open-source solvers (such as TensorFlow and Apache MXNet), toolkits (such as RDKit cheminformatics software), and open-scientific initiatives (such as DeepChem) makes it easy to use these frameworks in daily research. In the field of chemical informatics, many […]

Deep learning has revolutionized how we process and handle real-world data. There are many types of deep learning applications, including applications to organize a user’s photo archive, make book recommendations, detect fraudulent behavior, and perceive the world around an autonomous vehicle. In this post, we’ll show you step-by-step how to use your own custom-trained models […]

Millions of customers shop in brick and mortar stores every day. Currently, most of these retailers have no efficient way to identify these shoppers and understand their purchasing behavior. They rely on third-party market research firms to provide customer demographic and purchase preference information.

This blog post walks you how you can use AWS services to identify purchasing behavior of your customers. We show you:

How retailers can use captured images in real time.
How Amazon Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use Amazon Athena and Amazon QuickSight to analyze the face attributes.
How you can create unique insights and learn about customer emotions and demographics.
How to implement serverless architecture using AWS managed services.

Update: On 17 JUL 2018, Amazon Polly released Asynchronous Synthesis, a new feature that enables you to process up to 100,000 characters of text at a time using asynchronous requests. Read this article for details: Amazon Polly Update – Time-Driven Prosody and Asynchronous Synthesis. Amazon Polly, one of AWS’s first AI services, turns text into lifelike […]