It’s free and open source, works onWindows, Mac, and Linux. RStudio IDE – powerful user interface for R.R is a free software environment for statistical computing and graphics.Hortonworks Sandbox is a personal, portable Hadoop environment that comes with a dozen interactive Hadoop tutorials.Datalab from Google easily explore, visualize, analyze, and transform data using familiar languages, such as Python and SQL, interactively.The following sections contain some excellent resources from the following sites (it is recommended to clone these git sources to stay in sync with the latest updates): Over 150 of the Best Machine Learning, NLP, and Python Tutorials (June 2017): An introduction to convolutional neural networks in TensorFlow.Flappy Bird – Deep Reinforcement Learning (Deep Q-Network).https :////A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks /.https :///1/intuitive-explanation-convnets/.tensorboard –logdir=’/tmp/mnist_tutorial/’.https :// /fchollet/keras/tree/master/examples (MNIST)./leriomaggio/deep-learning-keras-tensorflow.Introduction to Deep Neural Networks with Keras and Tensorflow./aymericdamien/TensorFlow-Examples (MNIST)./blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd. Learn TensorFlow and deep learning, without a Ph.D.Herewith some of the demo material linked to the MIIA Events on 3 &. See also Bloomberg Beta’s 2016 machine intelligence landscape on the Machine Intelligence Landscape page and the machine intelligence ecosystem for more details.įigure 1 Some example platforms and tech user tools that can be utilised in Research, Technology and Application related projects There are also a range of data science and machine learning tools and platforms available that can be utilised such as IBM Watson, AlchemyAPI, Oxdata H2O, Azure Machine Learning & Cortana Analytics, Rapidminer, Dato, Cortical.io, Domino, MetaMind, etc. via a range of open source programming and scripting languages and rich libraries (see, for example, rich ecosystems of cutting-edge packages provided by Python and R) as well as collaborate on open source Machine Intelligence projects such as TensorFlow, Spark MLlib, Theano & Pylearn2, Caffe, Torch7, Numenta, Scikit-Learn, DeepLearning4j, MS DMLT, etc. We envisage groups within the MIIA community to utilise a broad range of machine intelligence technologies which also includes, for example, Deep Learning, Deep Reinforcement Learning, Probabilistic Graphical Models, Hierarchical Temporal Memory, etc. The approaches discussed cover the whole educational range, from elementary school to the university level, in both formal as well as informal settings.Figure 1 illustrates some example platforms and tech user tools that can be utilised in research and application related projects via international & intra-African collaboration. In addition, the book presents evaluation results regarding the impact of robotics on students’ interests and competence development. This also involves the introduction of technologies ranging from robotics controllers to virtual environments. The content will appeal to both researchers and educators interested in methodologies for teaching robotics that confront learners with science, technology, engineering, arts and mathematics (STEAM) through the design, creation and programming of tangible artifacts, giving them the chance to create personally meaningful objects and address real-world societal needs. This proceedings volume highlights the latest achievements in research and development in educational robotics, which were presented at the 8th International Conference on Robotics in Education (RiE 2017) in Sofia, Bulgaria, from April 26 to 28, 2017.
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