Science Courses and Tutorials
Science education websites including university courses online, massive open online courses, and tutorials. No commercial sites.
344 listings
Submitted Jan 02, 2017 to Science Courses and Tutorials Taught in 2011 by Profs. Erik Demaine and Srinivas Devadas, Introduction to Algorithms provides an introduction to algorithms and data structures, taught in Python and following the classic textbook by Cormen, Leiserson, Rivest, and Stein (2009). Downloadable course materials include lecture videos, assignments, quizzes, and solutions, and more.
|
Submitted Jan 02, 2017 to Science Courses and Tutorials The American Mathematical Society (AMS) maintains an online search tool for finding graduate programs in mathematical sciences throughout the U.S. and Canada. Search options include Masters and Ph.D. specialties, location, and more.
|
Submitted Jan 01, 2017 to Science Courses and Tutorials For folks who want to get started with Docker, there is the initial hurdle of installing Docker. Even though Docker has made it extremely simple to install Docker on different OS like Linux, Windows and Mac, the installation step prevents folks from getting started with Docker. With Play with Docker, that problem also goes away. Play with Docker provides a web based interface to create multiple Docker hosts and be able to run Containers. This project is started by Docker captain Marcos Nils and is an open source project.
|
Submitted Dec 29, 2016 to Science Courses and Tutorials While research in Generative Adversarial Networks (GANs) continues to improve the fundamental stability of these models, we use a bunch of tricks to train them and make them stable day to day. Here is a summary of some of the tricks.
|
Submitted Dec 29, 2016 to Science Courses and Tutorials I recently published a bit of self-promotion in the form of an animation based on the new geom_voronoi_tile() and geom_voronoi_segment() functions in ggforce. This prompted someone to ask for the source code and I off-handedly said I would make a blog post about. So here we are, but I’m not going to show you how I made that animation (sorry). Instead I’m going to show you how to make your very own animated Christmas card using ggplot2, ggforce, and tweenr (as well as mgcv and deldir for some computations).
|
Submitted Dec 29, 2016 to Science Courses and Tutorials A deep Q learning demonstration using Google TensorFlow.
|
Submitted Dec 29, 2016 to Science Courses and Tutorials A Udacity course to learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling to data visualization. If you present data to others, aspire to be an analyst or data scientist, or if you’d like to become more technical with visualization tools, then you can grow your skills with this course.
The course does not cover exploratory approaches to discover insights about data. Instead, the course focuses on how to visually encode and present data to an audience once an insight has been found. |
Submitted Dec 28, 2016 to Science Courses and Tutorials A tutorial on random forests in the Go programming language.
|
Submitted Dec 24, 2016 to Science Courses and Tutorials Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.
|
Submitted Dec 23, 2016 to Science Courses and Tutorials This repository contains the exercises for the EPFL master course EE-558 A Network Tour of Data Science (moodle). The Data Scientist toolkit, a set of tools, mostly in Python, to help during the Data Science process. Machine Learning (ML) & Graph Signal Processing (GSP) algorithms. These exercises are designed so as to familiarize yourself with the algorithms presented in class.
|
Submitted Dec 22, 2016 to Science Courses and Tutorials Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. This course is taught by Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team.
|
Submitted Dec 22, 2016 to Science Courses and Tutorials Statistical Mechanics: Algorithms and Computations is a Coursera online course taught by Werner Krauth, Director of the French National Center for Scientific Research, and created by École Normale Supérieure. The course covers a lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.
|
Submitted Dec 22, 2016 to Science Courses and Tutorials Every course on Coursera is taught by top instructors from the world’s best universities and educational institutions. Courses include recorded video lectures, auto-graded and peer-reviewed assignments, and community discussion forums. When you complete a course, you can receive a sharable electronic Course Certificate for an extra fee.
|
Submitted Dec 22, 2016 to Science Courses and Tutorials Udacity was born out of a Stanford University experiment in which Sebastian Thrun and Peter Norvig offered their "Introduction to Artificial Intelligence" course online to anyone, for free. Over 160,000 students in more than 190 countries enrolled and not much later, Udacity was born. Take a single course or earn a Nanodegree in topics such as artificial intelligence, VR development, and self-driving car engineering.
|
Submitted Dec 22, 2016 to Science Courses and Tutorials Founded by Harvard University and MIT in 2012, edX is an online learning destination and MOOC provider, offering high-quality courses from the world’s best universities and institutions to learners everywhere. edX was founded by and continue to be governed by colleges and universities. It is the only leading MOOC provider that is both nonprofit and open source.
|
Submitted Dec 21, 2016 (Edited Jan 11, 2017) to Science Courses and Tutorials Useful set of modules and notes from the STA 663 Computational Statistics in Python course taught at Duke (http://people.duke.edu/~ccc14/sta-663-2015/).
The goal of STA 663 is to learn statistical programming - how to write code to solve statistical problems. In general, statistical problems have to do with the estimation of some characteristic derived from data - this can be a point estimate, an interval, or an entire function. Almost always, solving such statistical problems involves writing code to collect, organize, explore, analyze and present the data. For obvious reasons, we would like to write good code that is readable, correct and efficient, preferably without reinventing the wheel. |
Submitted Dec 21, 2016 (Edited Jan 16, 2017) to Science Courses and Tutorials Take a whirlwind tour of your next favorite programming language, tool, or algorithm. Community-driven!
|
Submitted Dec 21, 2016 to Science Courses and Tutorials Tutorials, code, and data for learning python with examples applied to oceanography.
|
Submitted Dec 21, 2016 to Science Courses and Tutorials This is a short overview of how Python is used in science and particularly in geosciences. The idea is to show how to setup Python for purposes of scientific computation and visualization, cover some basic features of the language and show some of the real world applications. This overview is aimed at the scientists who are interested in Python but don't know how to start using it (this might be really confusing for a newcomer), or would like to learn more about possible Python applications. For those of you who already use Python there will be nothing really new.
|
Submitted Dec 21, 2016 to Science Courses and Tutorials This by Robert Herrmann tutorial provides a complete data set for regional moment tensor inversion.
|