There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.
Nando de Freitas is a "machine learning professor at Oxford University, a lead research scientist at Google DeepMind, and a Fellow of the Canadian Institute For Advanced Research (CIFAR) in the Neural Computation and Adaptive Perception program." Above, you can watch him teach an Oxford course on Deep Learning, a hot subfield of machine learning and artificial intelligence which creates neural networks--essentially complex algorithms modeled loosely after the human brain--that can recognize patterns and learn to perform tasks. To complement the 16 lectures you can also find lecture slides, practicals, and problems sets on this Oxford web site. If you'd like to learn about Deep Learning in a MOOC format, be sure to check out the new series of courses created by Andrew Ng on Coursera. Oxford's Deep Learning course will be added to our list of Free Online Computer Science Courses, part of our meta collection, 1,300 Free Online Courses from Top Universities.
This presentation will review how 360º photography is rapidly changing the way DPR Construction documents both existing conditions and ongoing progress on job sites. We will discuss new workflows related to progress documentation and its benefits. For example, we'll cover scheduling of documentation on a weekly and/or milestone basis to enable virtual quality assurance/quality control walks with architects, engineers, and inspectors. We'll also review workflows for capturing conversations that revolve around actual project locations to assist with radio frequency interference (RFI) creation. We will discuss use for risk mitigation, including documenting existing conditions for design planning/bidding, as well as capture of MEP (mechanical, electrical, and plumbing) rough-in before dry-wall and ceiling close up.
The Madrid ASDM summer school is in its thirteenth edition this year, with hundreds of students from all over the world having attended so far. It comprises 12 intensive (15 lecture hours) week-long courses, and a student may attend from one up to six courses. The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series. Each course has theoretical and practical classes, the latter done with R or python. While the summer school is mainly attended by people from academia - PhD students and researchers-, people from the industry also assist.
Numerical linear algebra is concerned with the practical implications of implementing and executing matrix operations in computers with real data. It is an area that requires some previous experience of linear algebra and is focused on both the performance and precision of the operations. In this post, you will discover the fast.ai Computational Linear Algebra for Coders Review Photo by Ruocaled, some rights reserved. The course "Computational Linear Algebra for Coders" is a free online course provided by fast.ai.
Industrial robots are primarily known from the automotive industry's production lines. The goal of this class is to present robots instead as multifunctional and flexible interfaces between the digital and the physical world that can be used for anything from innovative, large-scale fabrication to immersive virtual reality (VR) simulators. This extension beyond the robots' initial scope is enabled by new software developments that facilitate a seamless workflow from design to machine through Dynamo software and KUKA prc. Utilizing parametric design tools lets us use robots for mass customization and small lot sizes, rather than mass fabrication. The class will provide an overview on how to utilize industrial robots through Dynamo and Fusion 360 software, and present realized projects by both small to medium-size enterprises as well as international corporations.
Concepts in the book are laid out clearly, often with diagrams, but the book moves quickly. The book expects you to keep up or you will fall behind. That being said, each section has an overview of the concepts to be covered and ends with worked examples and quiz questions, the answers to which are available on the book's website. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.
Whether you you need guidance on learning to code or you are a seasoned machine learning practitioner, Google is here to help. The search engine giant has come up with a new course -- 'Learn with Google AI' -- that acts as a practical introduction to machine learning to all users for free. Machine learning (ML) is a branch of artificial intelligence (AI) and pertains to a computer's ability to execute certain tasks on its own without being programmed by a human being. Some examples of ML include self-driving cars, speech recognition, language translators, etc. Google's new machine learning crash course is designed to provide a fast-paced self-study guide for aspiring machine learning practitioners using high-level TensorFlow (TF) APIs. It features a series of video lessons with lectures from ML experts, real-world case studies and hands-on practice exercises to help users learning about key ML algorithms and frameworks.
Artificial intelligence is gaining interest and ground, with major players investing heavily in areas as disparate as drone footage analysis, app development platforms and personal digital assistants. Google is investing in the educational side of AI, as well. The tech giant recently announced a free 15-hour machine learning training course, aimed at users of all experience levels (though knowledge of introductory algebra and some proficiency in programming basics and Python will come in handy). Machine Learning Crash Course (MLCC) is designed to help users develop skills in artificial intelligence and machine learning through free lessons, tutorials and hands-on exercises, the company announced. "We believe that the potential of machine learning is so vast that every technical person should learn machine learning fundamentals," wrote Barry Rosenberg, with the Google Engineering Education Team, on the Google Developers blog.