Deep learning refers to artificial neural networks that are composed of many layers. Deep learning is a powerful set of techniques for finding accurate information from raw data. This tutorial will teach you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. Each section in this course provides a clear and concise introduction of a key topic, one or more example of implementations of these concepts in R, and guidance for additional learning, exploration, and application of the skills learned therein. You will start by understanding the basics of Deep Learning and Artificial neural Networks and move on to exploring advanced ANN's and RNN's.

Learn the basics of Deep Learning with hands on exercises using the Caffe deep learning framework and the DIGITS visual interface. Build your own model and start classifying images. Artificial intelligence, machine learning and deep learning are in the news and all around us. They give us the promise of computers solving tasks that until recently were very hard for computers: speech recognition, translation, object recognition, image classification, autonomous driving cars. Caffe framework is free, open sourced, continuously improved, has good documentation and even has an entire zoo of pre trained deep neural network models for image classification and other computer vision tasks.

TensorFlow has been gaining immense popularity over the past few months, owing to its power and ease of use. This video aims to help you leverage the power of TensorFlow to perform image processing. Beginning with an introduction to image processing, the video will take you through TensorFlow's API-like graph tensor, which can be used for image classification. Starting off with basic 2D images, the video will gradually take you through recognizing more complex images, colors, shapes, and so on. Making use of the Python API, you will move on to classifying and training your model to identify more complex images such as face and expression detection, while you will also perform classification using regression.

We've designed these tutorials so that you can traverse the curriculum in more than one way. This evolving creature is a collaborative effort (see contributors tab).

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.

Do you want to build complex deep learning models in Keras? Do you want to use neural networks for classifying images, predicting prices, and classifying samples in several categories? Keras is the most powerful library for building neural networks models in Python. In this course we review the central techniques in Keras, with many real life examples. We focus on the practical computational implementations, and we avoid using any math.

Deep learning and IoT are two game-changing technologies that have the potential to revolutionize the stakes for oil and gas companies facing profitmaking pressure in the face of the dramatic drop in price of oil. In this blog, based on Flutura's extensive experience in the oil and gas industry, we have highlighted three practical use cases, from the trenches, where these technologies are practically applied to solve real-life problems and impact meaningful business outcomes. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications. You forgot to provide an Email Address.

You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Online education provider Udacity, which is owned by Google X and Kitty Hawk founder Sebastian Thrun, has announced two new'nanodegrees'. One course will teach users the basics of driverless car engineering, while another will show students how to make systems for autonomous flying vehicles. You can now learn how to build a flying car in just four months thanks to a new $400 (£295) online course. Education provider Udacity has announced two new'nanodegrees' teaching users to make driverless or flying vehicles, such as the AeroMobil car pictured here Students will learn the basics of autonomous flight, including vehicle state planning and estimation, as well as motion planning.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.