Word embeddings provide a dense representation of words and their relative meanings. They are an improvement over sparse representations used in simpler bag of word model representations. Word embeddings can be learned from text data and reused among projects. They can also be learned as part of fitting a neural network on text data. In this tutorial, you will discover how to use word embeddings for deep learning in Python with Keras.

Artificial intelligence has the potential to improve people's lives in profound ways -- from helping diagnose diseases and breaking down language barriers to making businesses more efficient. Google believes that AI will help tackle huge challenges like healthcare, environmental protection and other social and developmental problems, while also spurring innovation for businesses and developers. The opportunity is huge and not constrained by location – a company in Bangalore or Gurgaon could serve the whole world. In fact, a recent report by Accenture concluded that India, by embracing AI technologies could add nearly $1 trillion to its GDP by 2035. India already has some of the key ingredients to becoming a major force in leading the next generation of disruptive innovation in machine learning (ML): a tech-savvy talent pool, renowned universities, healthy levels of entrepreneurship and strong corporations.

Computer vision has become one of the largest computer science research communities. We have made tremendous progress in recent years over a wide range of areas, including object recognition, image understanding, video analysis, 3D reconstruction, etc. It has also become one of the largest computer science research community. However, despite the expansion of our field, the percentage of female faculty members and researchers both in academia and in industry is still relatively low. As a result, many female researchers working in computer vision may feel isolated and do not have a lot of opportunities to meet with other women. The half-day workshop on Women in Computer Vision is a gathering for both women and men working in computer vision.

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.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )

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.