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.

In this tutorial, we will introduce you to Machine Learning with Apache Spark. The hands-on lab for this tutorial is an Apache Zeppelin notebook that has all the steps necessary to ingest and explore data, train, test, visualize, and save a model. We will cover a basic Linear Regression model that will allow us perform simple predictions on a sample data. This model can be further expanded and modified to fit your needs. Most importantly, by the end of this tutorial, you will understand how to create an end-to-end pipeline for setting up and training simple models in Spark.

Haven't taken this particular course. I will say that fast.ai You might not learn everything you will eventually need here, but you will learn how to learn what you need:).

WEBINAR DESCRIPTION This 1 hour session will provide an overview on Multi-layer Artificial Neural Networks (ANNs). Artificial Neural Networks (ANNs) are the building blocks of modern Deep Learning applications, such as image processing, speech recognition, text analytics, driverless cars etc. This session will cover the basics of multi-layer ANNs, discuss forward propagation and backpropagation logic, cost function in a multi-layer ANN and how to achieve convergence. This session will also include demonstration of small python programs which implement such Multi-Layer ANNs. WARNING:- This is an advanced Deep Learning topic.

Tesla's Model 3 sedans are already pretty futuristic, with self-driving technology, quick-charging batteries and a massive touchscreen in the front seat. Now, Tesla owners can add voice controls to the list. The firm added new Siri integration to the latest version of the Tesla app. It lets Model 3 owners remotely flash their car's lights, check the vehicle's battery levels and even lock or turn on the car. The latest update to the Tesla app added voice integration with Siri to the Model 3. It allows users to unlock/lock their car, locate their vehicle and more using the digital assistant.

Microsoft Cognitive Service service APIs enable the fastest route for businesses to integrate AI into their new or existing applications and systems. If you want to learn how to get started using the Microsoft Cognitive Services API, Microsoft Virtual Academy (MVA) has put together a series that dives into many of the most commonly used services. Follow the links below to view course content for each of the sections.

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.

We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gra- dient (SUCAG), for finite sum optimization problems. SUCAG constitutes an unbiased total gradient tracking technique that uses Hessian information to accelerate convergence. We an- alyze our method under the general asynchronous model of computation, in which functions are selected infinitely often, but with delays that can grow sublinearly. For strongly convex problems, we establish linear convergence for the SUCAG method. When the initialization point is sufficiently close to the optimal solution, the established convergence rate is only dependent on the condition number of the problem, making it strictly faster than the known rate for the SAGA method. Furthermore, we describe a Markov-driven approach of implementing the SUCAG method in a distributed asynchronous multi-agent setting, via gossiping along a random walk on the communication graph. We show that our analysis applies as long as the undirected graph is connected and, notably, establishes an asymptotic linear convergence rate that is robust to the graph topology. Numerical results demonstrate the merit of our algorithm over existing methods.

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.