Record and run settings a team which includes 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data processing jobs. Relational Databases are so stuffy and old! Welcome to HBase – a database solution for a new age. HBase: Do you feel like your relational database is not giving you the flexibility you need anymore?

Discussions on artificial intelligence have been around for way too long. And waiting for all that work that brings results it was like waiting for a new Game of Thrones season, facing up to more speculations than actual, official spoilers. Now it's here, AI we mean.

We can see that there is a bleeding of ideas between fields and subfields in statistics. The machine learning practitioner must be aware of both the machine learning and statistical-based approach to the problem. This is especially important given the use of different terminology in both domains. In his course on statistics, Rob Tibshirani, a statistician who also has a foot in machine learning, provides a glossary that maps terms in statistics to terms in machine learning, reproduced below.

To gain competitive advantage from AI, leaders must consider the technology's current state prior to aligning their strategic goals with AI initiatives. Our webinar will help you do that and guide you toward getting the most out of AI's potential. Many business leaders are contemplating whether and/or how to introduce artificial intelligence into their organizations. The challenges of implementing AI are much discussed, but beyond implementation there is the far more important question: How do we generate competitive advantage from AI's application? Using industry examples and findings from the Institute's research, he offers strategies for how to get the most out of AI's potential.

The skill or prediction error of a model must be estimated, and as an estimate, it will contain error. This is made clear by distinguishing between the true error of a model and the estimated or sample error. One is the error rate of the hypothesis over the sample of data that is available. The other is the error rate of the hypothesis over the entire unknown distribution D of examples.

Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google's Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms. While YouTube videos remain the main learning source and a key starting point for beginners, there is a slew of resources, especially books that can help cement fundamental concepts.

Like Mathematics and Computer Science, it is quickly becoming a tool which is widely used to make everything more effective and efficient, ranging all the way from websites to medical diagnosis. Today, I'm happy to announce the free Machine Learning Course on Commonlounge. Apart from tutorials on ML concepts and algorithms, the course also includes end-to-end follow-along examples, quizzes, and hands-on projects. Once done, you will have an excellent conceptual and practical understanding of machine learning and feel comfortable applying machine learning thinking and algorithms in your projects and work. This tutorial introduces what machine learning is.

Complete Guide to TensorFlow for Deep Learning with Python by Jose Portilla will help you learn how to use Google's Deep Learning Framework, TensorFlow with Python. This Deep Learning TensorFlow course is for Python developers who want to learn the latest Deep Learning techniques with TensorFlow. You will understand how Neural Networks work. Then you will build your own Neural Network from scratch with Python. This Deep Learning TensorFlow tutorial will teach you to use TensorFlow for Classification and Regression Tasks.

The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.

While artificial intelligence AAAI-19 will comprise a host of programs, well as strong outreach programs for including the Senior Member (AI) and human-computer interaction students, women, and sister conferences. Track, the Technical Demonstration (HCI) represent traditional They have absorbed all former Program, the Tutorial and Workshop mainstays of the conference, HCOMP special tracks into the main conference Programs, and several student programs, believes strongly in inviting, fostering, technical program, with provision for such as the Student Abstract and promoting broad, interdisciplinary distinguished oversight of reviews for and Poster Program and the Doctoral research. This field is particularly these areas.