Size belki şaka gelecek ama sizin torununuz ya da torununuzun çocuğu geçmiş hakkında konuşurken sizin bir ev, araba, vb… satın alma nedeninizi anlamayacak.
Çünkü onun dünyasında bireysel satın alma kavramı olmayacak, gelecekte insanlar hiçbirşeyi satın almayacaklar herşeyi kiralayacaklar…
Herşey ama herşey kullandığın kadar öde kavramı içerisinde yer alacak. İşte bunun kanıtı niteliğinde bir çalışma…
I finished my session “AI in Dynamics 365” in Microsoft Lisbon for #365Saturday. Thank to all participants.
In this session, I talked about AI solutions in Dynamics. “AI in Dynamics 365” session focuses on demos what is AI, what can it do, and cannot do.
This session will cover below AI solutions of Microsoft;
Dynamics 365 Sales Insights: Increase sales and improve decision making with AI-powered insights fueled by customer data.
Dynamics 365 Customer Insights: Gain a 360-degree view of customers and discover insights that drive personalised customer experiences.
Dynamics 365 Customer Service Insights: Leverage AI-driven insights to make better decisions and proactively improve customer satisfaction with confidence.
Dynamics 365 Saturday is a free Technical & Strategy Event Organised by the Microsoft Dynamics Community MVP’s For CRM and ERP professionals, technical consultants & developers. Learn & share new skills whilst promoting best practices, helping organisations overcome the challenges of implementing a successful digital transformation strategy with Microsoft Dynamics 365.
Dynamics 365 Saturday will replace CRM Saturday to provide a single platform to serve the whole Dynamics 365 community, the core customer experience values and ethics of CRM Saturday will continue to live on through 365 Saturday with the rest of the Dynamics Community.
Machine Learning konusuna ilgi duyanlar guzel bir kaynak.
It is a great resource for Machine Learning.
Machine learning gives computers the ability to learn without being explicitly programmed for the task at hand. The learning happens when data is combined with mathematical models, for example by finding suitable values of unknown variables in the model.
The most basic example of learning could be that of fitting a straight line to data, but machine learning usually deals with much more flexible models than straight lines.
The point of doing this is that the result can be used to draw conclusions about new data, that was not used in learning the model.
If we learn a model from a data set of 1000 puppy images, the model might — if it is wisely chosen — be able to tell whether another image
Erken kalkan yol alir diye bosuna soylemiyorlar. Erken kalkmanin ozellikle yogun calisanlar icin bir cok avantaji var.
Bu 10 adim/ogut aslinda sadece erken kalkmak icin degil genele de uyarlanabilir, diyet programi ya da daha duzenli calismak icin de benzer adimlari uygulayabilirsiniz. Bu tarz 10 adimlik listeler halinde bir isi devam ettirmek sizin o isi daha verimli ilerletmenizi ve tutarli olmanizi saglayacaktir.
Iste size 10 adim:
1. Bir neden bulun 2. Gerceklestirebilir hedefler belirleyin 3. Ilerlemenizi takip edin 4. Kendinizi odullendirin 5. Zamanlamanin onemini farkedin 6. Uyuma gunlugu tutun 7. Haftasonlari da programa sadik kalin 8. Cok detayli bir sekilde sabahlarinizi programlayin 9. Yediklerinizi hazmetme zamani olusturun 10. Tutarli olun
Machine Learning for Everyone makina ogrenmesinin temellerine inen ve konuyu basitçe anlatan güzel bir e-kitap.
Machine Learning for Everyone is a good ebook that goes to the basics of machine learning and simply tells the subject.
Classical machine learning is often divided into two categories – Supervised and Unsupervised Learning.
In the first case, the machine has a “supervisor” or a “teacher” who gives the machine all the answers, like whether it’s a cat in the picture or a dog. The teacher has already divided (labeled) the data into cats and dogs, and the machine is using these examples to learn. One by one. Dog by cat. Unsupervised learning means the machine is left on its own with a pile of animal photos and a task to find out who’s who. Data is not labeled, there’s no teacher, the machine is trying to find any patterns on its own. We’ll talk about these methods below. Clearly, the machine will learn faster with a teacher, so it’s more commonly used in real-life tasks. There are two types of such tasks: classification – an object’s category prediction, and regression – prediction of a specific point on a numeric axis.
Machine Learning use cases in Google, Facebook, Amazon, Microsoft, Kaggle, General Electric, and Cornerstone…
Big Data is a big thing and this case study collection will give you a good overview of how some companies really leverage big data to drive business performance.
They range from industry giants like Google, Amazon, Facebook, GE, and Microsoft, to smaller businesses which have put big data at the centre of their business model, like Kaggle and Cornerstone.
This case study collection is based on articles published by Bernard Marr on his LinkedIn Influencer blog.
Makine ogrenmesi konusunda teknik stratejileri anlatan 100 sayfalik bir kitap…
A 100-page book describing technical strategies for machine learning …
Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working on a machine learning application, and that you want to make rapid progress. This book will help you do so.
Example: Building a cat picture startup Say you’re building a startup that will provide an endless stream of cat pictures to cat lovers.
You use a neural network to build a computer vision system for detecting cats in pictures. But tragically, your learning algorithm’s accuracy is not yet good enough. You are under tremendous pressure to improve your cat detector. What do you do? Your team has a lot of ideas, such as: • Get more data: Collect more pictures of cats. • Collect a more diverse training set. For example, pictures of cats in unusual positions; cats with unusual coloration; pictures shot with a variety of camera settings; …. • Train the algorithm longer, by running more gradient descent iterations. • Try a bigger neural network, with more layers/hidden units/parameters. • Try a smaller neural network. • Try adding regularization (such as L2 regularization). • Change the neural network architecture (activation function, number of hidden units, etc.) • … If you choose well among these possible directions, you’ll build the leading cat picture platform, and lead your company to success. If you choose poorly, you might waste months. How do you proceed? This book will tell you how. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful
Customer engagement is about more than touchpoints. For sales organizations to be successful, it requires creating meaningful connections, building relationships, and nurturing relationships to establish trust. To build these relationships, sales professionals need robust and up-to-the-moment customer insights as well as the ability to collaborate effectively to deliver customer commitments.
Dynamics 365 for Sales and Microsoft Relationship enable sales reps to build deeper customer connections at scale using the power of Dynamics 365, LinkedIn, and Office 365. Sales professionals receive recommended content in addition to activities and notes through the sales playbook when working on an opportunity, helping to ensure they are using the right content for the right context.
The configure-price-quote capability enables sales professionals to efficiently put together the right product solution and quote the solution to customers. The ability to do simple forecasting will help in situations where external checks and adjustments need to be accounted for.
The enhancements to Dynamics 365 applications in the April ’19 release include hundreds of new capabilities across Sales, Marketing, Customer Service, Portal, Field Service, Project Service Automation, Finance and Operations, Talent, Retail, and Business Central. We’re adding a new set of mixed reality experiences using Microsoft Layout and Microsoft Remote Assist.
Microsoft Forms Pro is a brand new enterprise survey app built on top of Microsoft Forms and integrated with Dynamics 365 and Common Data Service for Apps.
The April ’19 release delivers continued investments in artificial intelligence capabilities that leverage the power of Microsoft AI research, tools, data, and the Power Platform to help organizations transform customer service, sales, and marketing functions. Dynamics 365 Sales Insights provides actionable insights to drive personalized engagement and proactive decision-making. Dynamics 365 Market Insights enables business users to gather actionable insights based on what consumers say, seek, and feel about their brands and products. Dynamics 365 Customer Service Insights optimizes the customer experience through AI enhanced analysis.
The April ’19 release introduces three brand new AI apps:
Dynamics 365 Virtual Agent for Customer Service provides AI-powered chat bots to optimize the customer experience.
Dynamics 365 Customer Insights enables every organization to unify and understand their customer data to harness it for intelligent insights and actions.
Dynamics 365 Fraud Protection enables the e-commerce merchants to drive down fraud loss, increase bank acceptance rates to yield higher revenue, and improve the online shopping experience.
Free Dynamics 365 Framework for Developers on GitHub
I have archived and there is no new development plan for my CubeXrmFramework which support for Dynamics CRM 2013, 2015 and 2016. This project downloaded from NuGet more than 1.000 times.
