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This is a single day course from 9:00am to 2:00pm. You will need to bring your laptop and have python, TensoFflow 1.0 and pandas installed before the class. You can find the instructions here. If you have any difficulties let us know before the day of the training and we will provide you with support. We will be running two parallel sessions, one for new users who have minimal or no experience with TensorFlow and another one for advanced users.

It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a $15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over $70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us. It's a challenge that requires every skill they've learned, plus many they haven't. Still, designers know the basics: The principles of user-centric design lay the groundwork for building a great AI system.

This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.

If you've ever wondered how two or more things relate to each other, or if you've ever had your boss ask you to create a forecast or analyze relationships between variables, then learning regression would be worth your time. In this article, you'll learn the basics of simple linear regression - a tool commonly used in forecasting and financial analysis. We will begin by learning the core principles of regression, first learning about covariance and correlation, and then moving on to building and interpreting a regression output. A lot of software such as Microsoft Excel can do all the regression calculations and outputs for you, but it is still important to learn the underlying mechanics. At the center of regression is the relationship between two variables called the dependent and independent variables.

Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C API. For this course, we will be using Python.

Schedule and Syllabus Unless otherwise specified the course lectures and meeting times are: Wednesday, Friday 3:30-4:20 Location: 200-219 This syllabus is subject to change according to the pace of the class.

He's been there for three weeks now -- though he is quick to point out that he was out of the office for one of them so it's really only been two weeks -- but that's not stopping Quartz from launching one of the Bot Studio's first experiments, a Twitter bot to help attendees of the NICAR conference. People going to #NICAR17 can ask a Twitter bot. Keefe says the Bot Studio, which is funded by a $240,000 grant from the Knight Foundation, will focus on two main areas: How bots and artificial intelligence can help journalists do their jobs better and how news consumers can use them to access news and information. Keefe joined Quartz after spending 16 years at WNYC. We spoke about the lessons he learned from public radio, his goals for the Bot Studio, and what it'll take for bots to catch on.

Do students learn as much when they read digitally as they do in print? For both parents and teachers, knowing whether computer-based media are improving or compromising education is a question of concern. With the surge in popularity of e-books, online learning and open educational resources, investigators have been trying to determine whether students do as well when reading an assigned text on a digital screen as on paper. The answer to the question, however, needs far more than a yes-no response. In my research, I have compared the ways in which we read in print and onscreen.

While understanding machine learning has never been easier, it helps to know how to get started on some of the latest tools and features that are available. In this hands-on workshop with Galvanize, you'll learn the very basics of machine learning via the language of Python. Our community of staff, students, and professional instructors will guide you through the elementary aspects of SciKit Learn, a popular machine learning library for the Python language. We will learn a bit more about matrices, develop a linear model on a real data set, and play around in the sandbox if we have time. You'll leave this session equipped to write your own scripts and feel more prepared to take the next steps on your path to understanding machine learning.