Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example.
Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.
In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a non-linear approach.
Linear regression is a linear system and the coefficients can be calculated analytically using linear algebra. ...
Linear regression does provide a useful exercise for learning stochastic gradient descent which is an important algorithm used for minimizing cost functions by machine learning algorithms.
Linear regression is a very simple approach for supervised learning. Though it may seem somewhat dull compared to some of the more modern algorithms, linear regression is still a useful and widely used statistical learning method. Linear regression is used to predict a quantitative response Y from the predictor variable X. Linear Regression is made with an assumption that there’s a linear relationship between X and Y.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression. When there are multiple input variables, literature from statistics often refers to the method as multiple linear regression.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
IBM Sterling Order Management System (OMS) is a comprehensive software solution that brokers orders across many disparate systems, orchestrates and automates cross-channel selling and fulfillment processes, and provides a global view of supply and demand across the supply chain.
It is a comprehensive B2C and B2B order management and fulfillment solution that addresses the complexities of fulfilling orders across multiple channels, while cost-effectively orchestrating global product and service fulfillment across the extended enterprise.
The IBM Sterling OMS solution provides a central source of order information, management, and monitoring, and provides a single order repository to enter, modify, track, cancel and monitor the entire order life cycle in real time. Your company can provide customers information about their orders, from any channel or division, when and where they need it.
In addition, your store personnel, call/contact center staff, website and field sales team can leverage the system to place or modify orders, determine order status, check inventory availability across all locations, and manage the returns process.
Single view of supply and demand across channels
Coordinated, customized fulfilment execution to support omni-channel needs
Single source of order information for accurate and timely updates
Integrated omni-channel order fulfilment processes for seamless customer experience
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
GraphQL queries access not just the properties of one resource but also smoothly follow references between them. While typical REST APIs require loading from multiple URLs, GraphQL APIs get all the data your app needs in a single request. Apps using GraphQL can be quick even on slow mobile network connections.
GraphQL APIs are organized in terms of types and fields, not endpoints. Access the full capabilities of your data from a single endpoint. GraphQL uses types to ensure Apps only ask for what’s possible and provide clear and helpful errors. Apps can use types to avoid writing manual parsing code.
GraphQL creates a uniform API across your entire application without being limited by a specific storage engine. Write GraphQL APIs that leverage your existing data and code with GraphQL engines available in many languages. You provide functions for each field in the type system, and GraphQL calls them with optimal concurrency.