Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. R Commander is a popular GUI-based "front-end" to the broad range of embedded statistical functionality in R software. R Commander is an'SPSS-like' GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK) which is known to have problems running on a Mac computer.

Do enterprises need a chief AI officer (CAIO) to shepherd their forward progress in a world of increasing automation? Sandy Carter, tech veteran and author of Extreme Innovation, said that "new roles like the CAIO will become essential for leveraging the big data coming into companies from all angles." Andrew Ng, Stanford computer science professor and cofounder of online learning provider Coursera, has also been a proponent of assigning an executive to transform data into value by making sure AI is applied across all data silos. But with the role still nascent, many experts continue to debate this topic. A quick public search on LinkedIn shows that only a dozen professionals currently hold the title.

Learn how to engineer features and build more powerful machine learning models. This is the most comprehensive, yet easy to follow, course for feature engineering available online. Throughout this course you will learn a variety of techniques used worldwide for data cleaning and feature transformation, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, a variety of techniques that you can apply to capture as much insight as you possibly can with the features of your data set. The course starts describing the most simple and widely used methods for feature engineering, and then describes more advanced and innovative techniques that automatically capture insight from your variables.

About this course: In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications Course objectives This course teaches participants the following skills: Use best practices for application development. Choose the appropriate data storage option for application data. Develop loosely coupled application components or microservices. Debug, trace, and monitor applications.

Get your team access to Udemy's top 2,000 courses anytime, anywhere. You'll start with the Neural Networks Review: You'll start your Convolutional Neural Networks endeavor by reviewing their history and motivation: You'll continue your Convolutional Neural Networks endeavor by going into all required concepts: Before jumping into code, you'll see some Convolutional Neural Networks action: Now it's time for you to code your own Convolutional Neural Networks app with your own images: Lastly, you can post questions or doubts, and I'll answer to you personally. Want to know how to navigate this course? For easier and prettier coding, install this Python IDE. You can work faster in PyCharm using these hotkeys, I'll use them in the course too This is the best stack for CNNs!

Today, natural language interfaces (NLIs) on computers or phones are often trained once and deployed, and users must just live with their limitations. Allowing users to demonstrate or teach the computer appears to be a central component to enable more natural and usable NLIs. Examining language acquisition research, there is considerable evidence suggesting that human children require interactions to learn language, as opposed to passively absorbing language, such as when watching TV (Kuhl et al., 2003, Sachs et al., 1981). Research suggests that when learning a language, rather than consciously analyzing increasingly complex linguistic structures (e.g. In contrast, the standard machine learning dataset setting has no interaction.

Driven by machine learning, the explosion of data has many companies feeling like they are being left behind. How can businesses derive value from these new technological developments? This course will address this issue and will help you understand what exactly machine learning and predictive analytics are, what are its limits and its potential risks, and why it may benefit your organization. Using real world case studies and many other examples of current and potential future industry usage, this course will help you better understand why many corporations are adopting, or should be adopting machine learning to better enable their future. Along the way you will learn the types of problems machine learning can solve, be conversant about the class of algorithms one can use, and the process for creating a successful project that incorporates machine learning.

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given text description, yielding low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. Second, an advanced multi-stage generative adversarial network architecture, StackGAN-v2, is proposed for both conditional and unconditional generative tasks. Our StackGAN-v2 consists of multiple generators and discriminators in a tree-like structure; images at multiple scales corresponding to the same scene are generated from different branches of the tree. StackGAN-v2 shows more stable training behavior than StackGAN-v1 by jointly approximating multiple distributions. Extensive experiments demonstrate that the proposed stacked generative adversarial networks significantly outperform other state-of-the-art methods in generating photo-realistic images.

Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

A global study finds several new categories of human jobs emerging, requiring skills and training that will take many companies by surprise. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. The threat that automation will eliminate a broad swath of jobs across the world economy is now well established. As artificial intelligence (AI) systems become ever more sophisticated, another wave of job displacement will almost certainly occur. It can be a distressing picture.