Samsung opens seventh AI centre in Montreal


Samsung has opened another artificial intelligence (AI) centre, this time in Montreal, Canada. It is Samsung's seventh AI centre in total since it set up its first in Seoul in November last year. It also marks its fourth in North America after Silicon Valley, New York, and Toronto. The city hosted leading AI researchers at McGill University and the University of Montreal who have had "longstanding relationships" with Samsung, with 250 researchers and 9,000 students in related programs. The South Korean tech giant said the Montreal centre would focus on machine learning, language, vision, and multi-modal interactions.

EdNet: A Large-Scale Hierarchical Dataset in Education Artificial Intelligence

With advances in Artificial Intelligence in Education (AIEd) and the ever-growing scale of Interactive Educational Systems (IESs), data-driven approach has become a common recipe for various tasks such as knowledge tracing and learning path recommendation. Unfortunately, collecting real students' interaction data is often challenging, which results in the lack of public large-scale benchmark dataset reflecting a wide variety of student behaviors in modern IESs. Although several datasets, such as ASSISTments, Junyi Academy, Synthetic and STATICS, are publicly available and widely used, they are not large enough to leverage the full potential of state-of-the-art data-driven models and limits the recorded behaviors to question-solving activities. To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with artificial intelligence tutoring system. EdNet contains 131,441,538 interactions from 784,309 students collected over more than 2 years, which is the largest among the ITS datasets released to the public so far. Unlike existing datasets, EdNet provides a wide variety of student actions ranging from question-solving to lecture consumption and item purchasing. Also, EdNet has a hierarchical structure where the student actions are divided into 4 different levels of abstractions. The features of EdNet are domain-agnostic, allowing EdNet to be extended to different domains easily. The dataset is publicly released under Creative Commons Attribution-NonCommercial 4.0 International license for research purposes. We plan to host challenges in multiple AIEd tasks with EdNet to provide a common ground for the fair comparison between different state of the art models and encourage the development of practical and effective methods.

On Education Deep Learning with TensorFlow 2.0 [2019] - all courses


Link: Deep Learning with TensorFlow 2.0 [2019] Data Science Deep Learning Machine-Learning Scientific Libraries ... Learn about the updates being made to TensorFlow in its 2.0 version. We'll give an ... 8,767 students enrolled Created by 365 Careers, 365 Careers Team Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Some basic Python programming skills You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course. All software and data used in the course are free. Data scientists, machine learning engineers, and AI researchers all have their own skillsets.

On EducationThe Data Science Course 2019: Complete Data Science - CouponED


BESTSELLER 4.5 (26,962 ratings) 122,893 students enrolled Created by 365 Careers, 365 Careers Team What you'll learn The course provides the entire toolbox you need to become a data scientist Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow Impress interviewers by showing an understanding of the data science field Learn how to pre-process data Understand the mathematics behind Machine Learning (an absolute must which other courses don't teach!) Start coding in Python and learn how to use it for statistical analysis Perform linear and logistic regressions in Python Carry out cluster and factor analysis Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn Apply your skills to real-life business cases Use state-of-the-art Deep Learning frameworks such as Google's TensorFlowDevelop a business intuition while coding and solving tasks with big data Unfold the power of deep neural networks Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations Requirements No prior experience is required. We will start from the very basics You'll need to install Anaconda. We will show you how to do that step by step Microsoft Excel 2003, 2010, 2013, 2016, or 365 Each of these topics builds on the previous ones. And you risk getting lost along the way if you don't acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics.