About this course: Welcome to the Reinforcement Learning course. Here you will find out about: - foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc. --- with math & batteries included - using deep neural networks for RL tasks --- also known as "the hype train" - state of the art RL algorithms --- and how to apply duct tape to them for practical problems.

Many people in the AI community immediately hailed it as a huge step forward in the combination of these techniques. While I'm always a bit skeptical of any general and immediate backlash against a technology that hasn't been used for bad there are very good points about this that should be discussed. Last week I started the online course on edX by Microsoft titled "Ethics and Law in Data and Analytics." What was most interesting to me was a very simple framework they look at values of ethics. It is broken into two value sets: 1) values based on the well being of others (in this case the person receiving the call at the business) and 2) values based on my own well-being (a mixture of Google and me).

The paper "Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network" and its source code is available here: https://arxiv.org/abs/1803.07835 A few comments with some of the best applications: Lowell Camp - "This technology could be used for consumer-budget markerless facial motion capture, and if a follow-up paper enhances it with audio analysis for tongue posing, then it would require very little touch-up beyond a little temporal filtering." Evan - "Could this be used for some kind of automatic lip-reading system for deaf viewers to view live events?" Idjles Erle - "Reconstructing ancestors faces from photos that are 150 years old. Working out from old photos who is more likely rested to whom."

Veronica is a senior editor for The Economist Intelligence Unit's thought leadership division in New York. She specialises in market environment topics and trends that cut across industries, including the future of work, technological disruption, and economic competitiveness. In addition to these areas, she has led projects on advancements in manufacturing, historic energy demand trends, and socioeconomic topics such as organised labour, post-war impact investing and growth of cities. Until July 2014 Veronica was the EIU's commerce and regulations analyst for 29 countries, mostly in the emerging markets. She has written for various EIU publications, on subjects such as financial inclusion, international trade, and policies aimed at attracting investment and promoting innovation. Veronica holds master's and bachelor's degrees in international relations from New York University and the University of Pennsylvania, respectively.

Python is a dynamic modern object -oriented programming language. It is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer. It is also known as a general purpose programming language due to it's flexibility.

Python is one of the most important programming language and must be part of any programmer repertoire worth their salt. Python as a general purpose high level programming language is known for its simplicity. It provides constructs which allows developer to create software with fewer lines of code enabling better readability and less complexity. It also supports multiple programming styles such as functional, object oriented, procedural and imperative. This course has been created for absolute beginners.

Visual introduction to Deep Learning based on simple deep neural network. Take this course if you want to understand the magic behind deep neural networks and to get a excellent visual intuition on what is happening under the hood when data is travelling through the network and ends up as a prediction at it's output. You will visually see what exactly they are doing and how neural network uses these components to come up with accurate predictions.

For the most AI online courses, they usually tech you how to use the tool and build up into your system. For my preseonal, I like this like course than "how to use" course. It is a base, so that we can have a theory backgoud to support more advanced topic and build up our AI system. Besides, teacher always use a simple example to show the "what algorithm is" it makes us very easy to understand that. For example, I knew A* manay years ago but can not understand what is heristic h and he use a MRT as an example, then I finallly understand it.

Update 23 Aug 2017: Do note that Quantopian platform will no longer support third party broker integration. Please see their website under forum. The title of the post is "Phasing Out Brokerage Integrations". This course provides you with the tools that top hedge funds used. These institutional tools include but are not limited to market data, fundamental data, sentiment analysis data, and more.

About this course: This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.