Soft Actor Critic—Deep Reinforcement Learning with Real-World Robots – The Berkeley Artificial Intelligence Research Blog We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google Brain, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. In this post, we will benchmark SAC against state-of-the-art model-free RL algorithms and showcase a spectrum of real-world robot examples, ranging from manipulation to locomotion.
Datasets for Data Mining and Data Science See also Data repositories Anacode Chinese Web Datastore: a collection of crawled Chinese news and blogs in JSON format. Populating MS Word Templates with Python Introduction In a previous post, I covered one approach for generating documents using HTML templates to create a PDF. While PDF is great, the world still relies on Microsoft Word for document creation.
Data Mining: Practical Machine Learning Tools and Techniques Machine learning provides practical tools for analyzing data and making predictions but also powers the latest advances in artificial intelligence. Our book provides a highly accessible introduction to the area and also caters for readers who want to delve into modern probabilistic modeling and deep learning approaches. Chris Pal has joined Ian Witten, Eibe Frank, and Mark Hall for the fourth edition of the book, and his expertise in these techniques has greatly extended its coverage. The book's online appendix provides a reference for the Weka software. SlidesTable of Contents
Five OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores — a larger-scale version of the system we built to play the much-simpler solo variant of the game last year. Using a separate LSTM for each hero and no human data, it learns recognizable strategies.
STHDA - Home Linear Regression Essentials in R Linear regression (or linear model) is used to predict a quantitative outcome variable (y) on the basis of one or mult... Interaction Effect in Multiple Regression: Essentials Europe Code Week 360° virtual tour creation with CoSpaces_Edu The objective of this lesson plan is to create different Virtual Reality brochures or touristic guides in which students show their knowledge of their community, their city, a culture, or a civilization. This lesson involves getting students to do a significant amount of research and data collection, organize the information they gathered, and decide how to best present it in narrated or written format.CoSpaces Edu allows students to use 360° degree images onto which complementary information can be added through different types of icons or shapes available on the web or on the platform itself.The use of this type of visuals and the creation of 360° walks or itineraries is absolutely fantastic for class. It offers a way of bringing students closer to distant or nearby places and offering them a full view of the place. Types:
Flow-based Programming In computer programming, Flow-Based Programming (FBP) is a programming paradigm, discovered/invented by J. Paul Rodker Morrison in the late '60s, that uses a "data processing factory" metaphor for designing and building applications. FBP defines applications as networks of "black box" processes, which communicate via data chunks (called Information Packets) travelling across predefined connections (think "conveyor belts"), where the connections are specified externally to the processes. These black box processes can be reconnected endlessly to form different applications without having to be changed internally.
Learning From Data - Online Course (MOOC) A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.
12 Sites and Apps for Learning to Code When the conversation amongst educators turns to programming, Scratch is often the first resource that is mentioned. Scratch allows students to program animations, games, and videos through a visual interface. Students create their programs by dragging together blocks that represent movements and functions on their screens. Make GNU Make is a tool which controls the generation of executables and other non-source files of a program from the program's source files. Make gets its knowledge of how to build your program from a file called the makefile, which lists each of the non-source files and how to compute it from other files. When you write a program, you should write a makefile for it, so that it is possible to use Make to build and install the program.
Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. However, the result is presented differently according to the used packages.
Bash Bash is the GNU Project's shell. Bash is the Bourne Again SHell. Bash is an sh-compatible shell that incorporates useful features from the Korn shell (ksh) and C shell (csh). Extract and Visualize the Results of Multivariate Data Analyses factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. However, the result is presented differently according to the used packages. To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and dimensionality reduction analysis - we developed an easy-to-use R package named factoextra.