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.

Abstract-- Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols. I. INTRODUCTION Nature provides many examples where the performance of a collective of limited beings exceeds the capabilities of one individual. Ants transport prey of the size no single ant could carry, termites build nests of up to nine meters in height, and bees are able to regulate the temperature of a hive.

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.

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.

User-generated content, bring your own device and big data were some of the fastest growing EdTech topics of 2016. Here are eight ideas and expert predictions for the year ahead. As well as placing a greater emphasis on STEM education, many schools and universities are working to cultivate skills like creativity and empathy, which are thought to be harder for machines to replicate. EdTechX Global co-founder, Benjamin Vedrenne-Cloquet, says'I think we will see future skills proofing in both primary and secondary schools.' 2. More Learning Outside the Classroom 2016 saw flipped learning become mainstream in many schools and there now exist many opportunities for digitally assisted learning that can happen from any location. Technology at school is now better linked with technology at home and many predict that 2017 will see further disintegration of the classroom walls.

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.

I've written two blog posts on evaluation--the broccoli of machine learning. Both types are important not only to data scientists but also to managers and executives, who must evaluate project proposals and results. To managers I would say: It's not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you. To this end, here I'll evaluate a machine learning project report.

Online learning portal Udacity launched its first 36-week "nanodegree" course for self-driving car engineering last year. There's a new, introductory course available now as well, focused on bringing students with minimal programming into the larger program. Even better, Udacity has partnered with Lyft (which has self-driving plans of its own) to provide scholarships to the intro course in order to increase diversity to the program. Lyft says that people "from all backgrounds and perspectives" should have the opportunity to contribute to the future of transportation in the form of self-driving cars. "Diversity is crucial for creating solutions that serve everyone, and ridesharing is for everyone," the company writes on its website.

Welcome to this course: Deep Learning - Learn Convolutional Neural Networks. Deep Learning has made some huge and significant contributions and it's one of the mostly adopted techniques in order to drive insights from your data nowadays. Convolutional neural networks have gained a special status over the last few years as an especially promising form of deep learning. Rooted in image processing, convolutional layers have found their way into virtually all subfields of deep learning, and are very successful for the most part. Convolutional Neural Networks are very similar to ordinary Neural Networks: they are made up of neurons that have learnable weights and biases.

Description: Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples!