Machine learning on images is revolutionizing healthcare, manufacturing, retail, and many other sectors. Many previously difficult problems can now be solved by training machine learning (ML) models to identify objects in images. Our aim in this book is to provide intuitive explanations of the ML architectures that underpin this fast-advancing field, and to provide practical code to employ these ML models to solve problems involving classification, measurement, detection, segmentation, representation, generation, counting, and more. Image classification is the "hello world" of deep learning. Therefore, this book also provides a practical end-to-end introduction to deep learning. It can serve as a stepping stone to other deep learning domains, such as natural language processing.
When you deploy intelligent search in your organization, two important factors to consider are access to the latest and most comprehensive information, and a contextual discovery mechanism. Many companies are still struggling to make their internal documents searchable in a way that allows employees to get relevant information knowledge in a scalable, cost-effective manner. A 2018 International Data Corporation (IDC) study found that data professionals are losing 50% of their time every week--30% searching for, governing, and preparing data, plus 20% duplicating work. Amazon Kendra is purpose-built for addressing these challenges. Amazon Kendra is an intelligent search service that uses deep learning and reading comprehension to deliver more accurate search results.
Cognitive retail is the future that's driving retail stores both online and offline establishments. Despite the steep learning curve involved by all the stakeholders in terms of implementations, it's the prospect of diverse benefits that is luring retail stores towards cognitive computing. In this new retail world, the cognitive consumer expects great services at all times. Brands must deliver on such expectations. Cognitive retail affects almost every aspect of a retail business from marketing to supply chain to IT, e-commerce and merchandising.
If you're wondering which company makes the best streaming players for the least amount of money, you might not expect the answer to be Walmart. Walmart's $25 Onn FHD Streaming Stick and $30 UHD Streaming Device both undercut the cheapest comparable Roku and Fire TV streamers, yet the hardware doesn't seem compromised despite the low price. Meanwhile, Google's Android TV software provides a slick streaming menu, powerful voice search, and the ability to cast video from your phone. They don't support Dolby Vision, Dolby Atmos, or HDR10, and I had trouble getting TV volume and power controls to work on the cheaper FHD Streaming Stick. But if that doesn't happen to you, and your streaming needs aren't overly demanding, Walmart's devices are surprisingly hard to beat.
Launched at AWS re:Invent 2020, Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With Pipelines, you can create, automate, and manage end-to-end ML workflows at scale. You can extend your pipelines to include steps for tasks performed outside of Amazon SageMaker by taking advantage of custom callback steps. This feature lets you include tasks that are performed using other AWS services, third parties, or tasks run outside AWS. Before the launch of this feature, steps within a pipeline were limited to the supported native SageMaker steps.
No matter what skill you're trying to master, learning from great instructors makes the process much easier. If you would like to improve your coding skills, you can pick up some high-quality training for less in the Semi-Annual Sale at Bleeping Computer Deals. For a limited time, you can save 60% on all the training below using promo code ANNUAL60. This collection of seven courses shows you how to build apps and games for both iOS and Android using C#. Along the way, you discover how to build multiplayer games with Unity and create a clone of Flappy Bird through beginner-friendly tutorials.
It's a great feeling to see our retailers reopen nationwide. As customers, we have waited to walk into our favorite stores, eagerly finding new merchandise and admiring new floor layouts. At the same time, retailers are keen to understand how customers interact inside their physical stores. Tech innovations, especially powered by AI, can reveal insights about customers that retailers may not see in plain sight. Today, I'm excited to focus on spatial intelligence -- technology that measures how people and objects move and interact in a given space.
Artificial Intelligence-powered product recommendations and a more scientific approach to data has seen IKEA lift average order value (AOV) by 2% worldwide. Here Albert Bertlisson, head of engineering at Edge at IKEA Retail (Ingka Group) explains how the company did it. "At IKEA we have multiple places in our customer journey in various channels where different kinds of personalisation can deliver a superior customer experience," he says. "After a while in the broader'recommendations' team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behaviour and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers."
In 1937, two decades after founding his first Piggly Wiggly, supermarket entrepreneur Clarence Saunders opened Keedoozle, a "fully-automated grocery store." Groceries were offered at a steep discount and sample items were displayed in glass cabinets. "To purchase, the customer will insert a key in a hole in the showcase beside the sample article, press a button," TIME Magazine reported at the time. "In the stockroom the proper article will drop on a conveyor belt leading to the cashier's desk. Simultaneously the purchase price is recorded on an adding machine. After all purchases are made, the customer sticks his key into the adding machine, gets his bill. Using another key, the cashier releases the purchases all wrapped for the customer."
Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.