RESEARCH TRIANGLE PARK, N.C., July 16, 2019 (GLOBE NEWSWIRE) -- JAGGAER, the world's largest independent spend management company, announces an upcoming webinar with a deep analysis of the actionable application of Artificial Intelligence (AI) in the procurement process. The webinar is scheduled for Tuesday, July 23rd, 11-11:45 AM ET: Register here. This webinar is designed to address AI in companies that have already integrated a program into their digital transformation roadmap, and companies that are in the midst of considering an investment. Through applying Big Data with AI, the procurement function becomes an important provider of insights and guidance for increasingly complex supply chains. The resulting information yields better and more precise decisions with lower costs, however arriving at this state requires procurement departments to initiate new ways of thinking and acting, to support the adoption of new technologies.

At the 2019 Semicon Conference Applied Materials (AMAT) had a day-long seminar focused on technology, particularly memory, for artificial intelligence (AI) applications. In addition to talks by AI experts, the company also talked about their tools for manufacturing magnetic random access memory (MRAM) as well as resistive random access memory (RRAM) and Phase Change Memory (PCM). We will talk about a workshop at Stanford in August will explore emerging memories enabling artificial intelligence, especially for embedded products, such as IoT devices. Gary Dickerson from Applied Materials gave a kick-off talk at the seminar. He talked about the growth of data and the importance of memory to support data centers as well as the edge.

Bankers are rushing to take Oxford University's courses on fintech, blockchain strategy, algorithmic trading, and artificial intelligence before robots take their jobs. More than 9,000 people from upwards of 135 countries have taken the online open courses, which focus on digital transformation in business, at the university's Saïd Business School, a spokesperson told Markets Insider. The fintech course, the first of five to be launched, has run 12 times and attracted nearly 4,300 students in less than two years. The average age of participants across the courses is 39, and two-thirds of them came from the financial services sector, suggesting experienced professionals are returning to school to understand how their industry is being disrupted and learn the skills needed to weather the changes. Bankers' fears of being replaced by robots are well founded.

This book starts with the essentials of turning on the basic hardware. It provides the capability to interpret your commands and have your robot initiate actions. In this second edition, you will learn more specifics on how to use the Raspberry Pi's GPIO pins to communicate with and control a wide range of additional hardware. Teaching you to use the Raspberry Pi from scratch, this book will discuss a wide range of capabilities that can be achieved with it. These capabilities include voice recognition, human-like speech simulation, computer vision, motor control, GPS location, and wireless control.

Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.

In this post, you will learn about the concept of R-Squared in relation to assess the performance of multilinear regression machine learning model with the help of some real-world examples explained in simple manner. Once we have built a multilinear regression model, the next thing is to find out the model performance. The model performance can be found out by calculating the value of the Residual Standard Error (RSE) or the value of R-Squared. Residual Standard Error can be defined as the difference between the mean value of the prediction made by the model and the population mean value. In this article, we will learn the technique of evaluating the model performance using the value of R-Squared. Let's take an example of a real-world scenario where we went out for shopping some sarees, for upcoming festival, with our Grandma.

I've invested a considerable amount of time taking numerous courses, so I dug into my emails to collect some of the suggestions I've doled out. First, it's worth addressing the extent to which a product manager even needs to understand how AI works in order to be effective. There is an endless stream of business articles about what AI is, what it does and how it is going to disrupt this and that, all of which is great, but I am talking about understanding how it works (e.g. As Marty Cagan pointed out in Inspired (a must-read), product managers can come from a variety of different vertical disciplines, including those that are not necessarily technical, such as marketing or sales. Can these individuals, or even product managers who come from engineering but don't necessarily have a background in AI, be successful managing AI products?

I came across all kinds of advice when I was looking for a data science internship. But surprisingly, not many people talk about how to land that internship. My learning journey during my internship with Analytics Vidhya was equal parts challenging and fulfilling. I realized how vast and complex data science is and how unprepared I was for a full-time role. My path to become a data scientist would have been far more arduous and difficult one if I hadn't first interned. Even for experience people – internships are a very effective way to break into data science. We have now seen so many successful transitions enabled by internships. If you are looking for tips to prepare yourself for a data science internship, then you've come to the right place! In this article, I've drawn on my experience on the key aspects you need to know to land your first internship in data science. Each section is filled with plenty of tips, tricks, and resources. It won't be easy – but you would know what needs to be done. If you are looking for a guided journey with mentorship – check out our Certified Program: Data Science for Beginners (with Interviews) .

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image. Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation. In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

Please join Ericsson's IoT Studio for an evening event, "Unlocking the Value: Machine Learning and IoT," on July 16, 2019. We look forward to hosting you at D-Fifteen, Ericsson's innovation and collaboration space in Santa Clara for an evening of discussion on how IoT and Machine Learning work together to create business value. At this event, you will have the opportunity to learn from experts, network with your peers, enjoy refreshments and participate in a raffle for the chance to win an Apple Watch. We will feature a panel of Machine Learning experts from top companies representing the end-to-end IoT technology stack. The panel will be moderated by Daniel Elizalde, Ericsson's VP, Head of IoT for North America and will focus on practical insights that can be applied to companies today.