Tags: 2014, 2015, 2016, 2017, 2018, 2020, A Europe 2020 Flagship Initiative, A/B testing, academia, accelerated advancement of technologies, Accuracy, action, activation function, active learning, actual quality of work, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI methods, analytical work, analytics, analytics space, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, association, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, become a professional, Bellman equation, bias (ethics/fairness), bias (math), Big Data Analytics, bigram, binary classification, binning, boosting, breakthroughs, broad spectrum, broadcasting, bucketing, calibration layer, candidate generation, candidate sampling, categorical data, centroid, centroid-based clustering, certifications, checkpoint, class, class-imbalanced dataset, classification, classification model, classification threshold, clipping, Cloud TPU, clustering, co-adaptation, collaboration, collaborative filtering, comments, Companies, competition will grow, complex, Computational neural networks, computer science background, computer vision, Conference, confirmation bias, confusion matrix, content, continue to study, Continuous delivery, continuous feature, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, data analysis, data augmentation, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), decision boundary, decision threshold, decision tree, decision trees, deep learning, deep model, deep neural network, Deep Q-Network (DQN), definitely, delivering, demand is going to grow, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, design programs, designing inclusive, develop, develop much-needed talent, develop successful products, Developers, development and innovative research, device, DevOps, Dialogue Bots, different hyperparameters, different initializations, different overall structure, dimension reduction, dimensions, discover, discoveries, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, do projects, domains, downsampling, DQN, dropout regularization, dynamic model, eager execution, early stopping, economic, Education, effectively implements, effort, embedding space, embeddings, empirical risk minimization (ERM), employment program, enable data scientists to predict, engine, engineering, ensemble, environment, Ethics of artificial intelligence, European leaders, European scientific superiority, events, excellent, experience, experimenter’s bias, Explaining of Israel, field of commerce, field of data science, financial tool, find a job, fine tuning, Fintech, first step, focus, forget gate, full softmax, fully connected layer, Future of AI, gain professional control in the field, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, genuine, get started, global competitive field, global scale, go to professional conferences, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great success, greedy policy, ground truth, group attribution bias, growing demand for data scientists, growth, hard work, hashing, Healthcare, helpful feedback, heuristic, hidden layer, hierarchical clustering, high professional level, High-ability researchers, high-quality, high-tech industry, highly skilled professionals, hinge loss, holdout data, HORIZON 2020, Horizon 2020 Programme, hyperparameter, hyperplane, i.i.d., ideas, image recognition, imbalanced dataset, implicit bias, important, improvement, in-group bias, inclusive growth, incompatibility of fairness metrics, increasing demand, independently and identically distributed (i.i.d), individual fairness, industry, industry expertise, industry tracks, Inference, innovation, innovation processes, innovative, innovative products, input function, input layer, instance, Intelligent robots, inter-rater agreement, internal training, interpretability, iot, item matrix, items, iteration, k-means, k-median, keep up to date, Keras, Kernel Support Vector Machines (KSVMs), knowledge, label, labeled example, Lack of deep knowledge, lambda, Large scale analytics, launching it in reality, layer, Layers API (tf.layers), leadership, leading experts, learn programming, learning rate, least squares regression, linear algebra, linear model, linear regression, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Machine ethics, MACHINE LEARNING, major innovative development and research program, majority class, make important decisions, make the most use of their data usage, many companies, many opportunities, market, marketing strategies, Markov decision process (MDP), Markov property, master the basics, master the professional literature, material, mathematical, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), medium-sized data scientists, MEPs, metric, Metrics API (tf.metrics), mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), more accurate, more and more in the coming years, more complex math (Proximal and others), more popular day by day, more profitable, most sought-after, multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, neuron, new projects, News, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, objective, objective function, offline inference, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), Opportunities, optimize, optimizer, organization, organizing conference, out-group homogeneity bias, outliers, output layer, Over time, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, perceptron, perfect situation, performance, perplexity, pipeline, policy, pooling, portfolio, positive class, post-processing, potential social implications, powerful, PR AUC (area under the PR curve), practical industry experience, practical practice, practical proof, pre-trained model, precision, precision-recall curve, prediction, prediction bias, predictive applications, predictive parity, predictive rate parity, premade Estimator, preprocessing, Presentation, Presentation material, prevent loss situations, pricing, prior belief, production schedules, professional data scientists, professional employees, professionals, program, projects, prove themselves, proxy (sensitive attributes), proxy labels, Publications, publish scientific papers, Q-function, Q-learning, quantile, quantile bucketing, quantization, queue, random forest, random policy, rank (ordinality), rank (Tensor), rater, re-ranking, real world, real-world domains, recall, recommendation system, recruit high-quality, Rectified Linear Unit (ReLU), recurrent neural network, Registration, regression, regression model, regularization, regularization rate, reinforcement learning, reinforcement learning (RL), relevant education, reliable results, remember, remember that the value is definitely worth the effort, replay buffer, reporting bias, representation, required, requires investment, requiring, Research, research and application, research and development, Research and innovation in responsible approach, research innovations, research track, researchers, Responsible research, Retail, return, reward, ridge regularization, RNN, Robot rights, robotics, ROC (receiver operating characteristic) Curve, root directory, Root Mean Squared Error (RMSE), rotational invariance, sampling bias, SavedModel, Saver, scalar, scaling, scarcity of data scientists, scientific academic background, scikit-learn, scoring, second step, selection bias, semi-supervised learning, sensitive attribute, sequence model, services, serving, session (tf.session), shape (Tensor), shortened training, sigmoid function, similarity measure, simulations, single, size invariance, sketching, smart, social challenges, softmax, sparse feature, sparse representation, sparse vector, sparsity, sparsity/regularization (Ftrl), spatial pooling, specific language, sponsorship, squared hinge loss, squared loss, start a career in data science, State, state-action value function, state-of-the-art, static model, stationarity, steadily increasing, step, step size, stochastic gradient descent (SGD), stride, structural risk minimization (SRM), study advanced statistics, subsampling, success, successful data science career, successful opportunities, summary, supervised learning, supervised machine learning, supply chain management, support vector machines, sustainable, sustainable research-based programs, synthetic feature, systematic organization, Systems for ML, tabular Q-learning, target, target network, teach, technical presentations, TECHNOLOGY, temporal data, Tensor, Tensor Processing Unit (TPU), Tensor rank, Tensor shape, Tensor size, TensorBoard, TensorFlow, TensorFlow Playground, TensorFlow Serving, termination condition, test set, tf.Example, tf.keras, the amount of data, The conference, the data they own, The EU Framework Programme for Research and Innovation, The EU Framework Programme for Research and Innovation Horizon 2020, the EU’s blueprint, The European Research Area, The European Union, the field of data science, The Horizon 2020, The Horizon 2020 program, The Innovation Union, The Next Framework Programme, The secret to success is practice and perseverance, The Summit, Threat to human dignity, time series analysis, timestep, topics, tower, TPU, TPU chip, TPU device, TPU master, TPU node, TPU Pod, TPU resource, TPU slice, TPU type, TPU worker, training, training set, trajectory, transfer learning, translational invariance, trigram, true negative (TN), true positive (TP), true positive rate (TPR), tutorial, unawareness (to a sensitive attribute), underfitting, unlabeled example, unsupervised learning, unsupervised machine learning, update frequency, upweighting, user matrix, validation, validation set, value, vanishing gradient problem, Wasserstein loss, Wealth, Weaponization of AI, weight, Weighted Alternating Least Squares (WALS), wide model, width, will continue to grow, Work Programme, worth, worth the effort, write code
About The Author
CODESIGN.BLOG
Horizon 2020
sherry 0 Data Science, Design, Engineering, Explaining of Israel, Technology,
Horizon 2020 Programme
The European Union has initiated a major innovative development and research program called Horizon 2020. The funding of the program is approximately €80 biliion from 2014 to 2020. This program ensures significant breakthroughs and discoveries by the collaboration between academia and industry. The Horizon 2020 program is a financial tool that effectively implements the Innovation Union, a Europe 2020 Flagship Initiative, aimed at ensuring European supremacy in the global competitive field.
The Horizon 2020 program is a powerful economic growth engine that creates jobs and has received the political support of European leaders and MEPs. Everyone agrees that this research and development is a good investment for our future and that is why it is at the heart of the EU’s blueprint for smart, sustainable and inclusive growth and employment program.
Through the development and innovative research, the Horizon 2020 program manages to produce excellent science, outstanding industrial leadership and success in addressing social challenges. The overarching goal is to ensure the removal of barriers to innovation, alleviation of the public and private sectors so that they can collaborate and work together and provide innovative products and services and above all ensure European scientific superiority on a global scale.
The Horizon 2020 program is open to everyone and is built on a simple structure that optimizes working time and allows participants to focus on the really important things. In this way, new projects are handled on a high professional level and manage to achieve quality results in a relatively short time.
The EU Framework Programme for Research and Innovation will be complemented by further measures to complete and further develop the European Research Area. These measures will aim at breaking down barriers to create a genuine single market for knowledge, research and innovation.
Presentation material
Research and innovation in responsible approach
Responsible research and innovation processes are carried out through proper assessment of potential social implications with a view to designing inclusive, innovative, sustainable research-based programs.
Tags: 2014, 2015, 2016, 2017, 2018, 2020, A Europe 2020 Flagship Initiative, A/B testing, academia, accelerated advancement of technologies, Accuracy, action, activation function, active learning, actual quality of work, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI methods, analytical work, analytics, analytics space, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, association, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, become a professional, Bellman equation, bias (ethics/fairness), bias (math), Big Data Analytics, bigram, binary classification, binning, boosting, breakthroughs, broad spectrum, broadcasting, bucketing, calibration layer, candidate generation, candidate sampling, categorical data, centroid, centroid-based clustering, certifications, checkpoint, class, class-imbalanced dataset, classification, classification model, classification threshold, clipping, Cloud TPU, clustering, co-adaptation, collaboration, collaborative filtering, comments, Companies, competition will grow, complex, Computational neural networks, computer science background, computer vision, Conference, confirmation bias, confusion matrix, content, continue to study, Continuous delivery, continuous feature, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, data analysis, data augmentation, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), decision boundary, decision threshold, decision tree, decision trees, deep learning, deep model, deep neural network, Deep Q-Network (DQN), definitely, delivering, demand is going to grow, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, design programs, designing inclusive, develop, develop much-needed talent, develop successful products, Developers, development and innovative research, device, DevOps, Dialogue Bots, different hyperparameters, different initializations, different overall structure, dimension reduction, dimensions, discover, discoveries, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, do projects, domains, downsampling, DQN, dropout regularization, dynamic model, eager execution, early stopping, economic, Education, effectively implements, effort, embedding space, embeddings, empirical risk minimization (ERM), employment program, enable data scientists to predict, engine, engineering, ensemble, environment, Ethics of artificial intelligence, European leaders, European scientific superiority, events, excellent, experience, experimenter’s bias, Explaining of Israel, field of commerce, field of data science, financial tool, find a job, fine tuning, Fintech, first step, focus, forget gate, full softmax, fully connected layer, Future of AI, gain professional control in the field, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, genuine, get started, global competitive field, global scale, go to professional conferences, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great success, greedy policy, ground truth, group attribution bias, growing demand for data scientists, growth, hard work, hashing, Healthcare, helpful feedback, heuristic, hidden layer, hierarchical clustering, high professional level, High-ability researchers, high-quality, high-tech industry, highly skilled professionals, hinge loss, holdout data, HORIZON 2020, Horizon 2020 Programme, hyperparameter, hyperplane, i.i.d., ideas, image recognition, imbalanced dataset, implicit bias, important, improvement, in-group bias, inclusive growth, incompatibility of fairness metrics, increasing demand, independently and identically distributed (i.i.d), individual fairness, industry, industry expertise, industry tracks, Inference, innovation, innovation processes, innovative, innovative products, input function, input layer, instance, Intelligent robots, inter-rater agreement, internal training, interpretability, iot, item matrix, items, iteration, k-means, k-median, keep up to date, Keras, Kernel Support Vector Machines (KSVMs), knowledge, label, labeled example, Lack of deep knowledge, lambda, Large scale analytics, launching it in reality, layer, Layers API (tf.layers), leadership, leading experts, learn programming, learning rate, least squares regression, linear algebra, linear model, linear regression, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Machine ethics, MACHINE LEARNING, major innovative development and research program, majority class, make important decisions, make the most use of their data usage, many companies, many opportunities, market, marketing strategies, Markov decision process (MDP), Markov property, master the basics, master the professional literature, material, mathematical, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), medium-sized data scientists, MEPs, metric, Metrics API (tf.metrics), mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), more accurate, more and more in the coming years, more complex math (Proximal and others), more popular day by day, more profitable, most sought-after, multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, neuron, new projects, News, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, objective, objective function, offline inference, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), Opportunities, optimize, optimizer, organization, organizing conference, out-group homogeneity bias, outliers, output layer, Over time, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, perceptron, perfect situation, performance, perplexity, pipeline, policy, pooling, portfolio, positive class, post-processing, potential social implications, powerful, PR AUC (area under the PR curve), practical industry experience, practical practice, practical proof, pre-trained model, precision, precision-recall curve, prediction, prediction bias, predictive applications, predictive parity, predictive rate parity, premade Estimator, preprocessing, Presentation, Presentation material, prevent loss situations, pricing, prior belief, production schedules, professional data scientists, professional employees, professionals, program, projects, prove themselves, proxy (sensitive attributes), proxy labels, Publications, publish scientific papers, Q-function, Q-learning, quantile, quantile bucketing, quantization, queue, random forest, random policy, rank (ordinality), rank (Tensor), rater, re-ranking, real world, real-world domains, recall, recommendation system, recruit high-quality, Rectified Linear Unit (ReLU), recurrent neural network, Registration, regression, regression model, regularization, regularization rate, reinforcement learning, reinforcement learning (RL), relevant education, reliable results, remember, remember that the value is definitely worth the effort, replay buffer, reporting bias, representation, required, requires investment, requiring, Research, research and application, research and development, Research and innovation in responsible approach, research innovations, research track, researchers, Responsible research, Retail, return, reward, ridge regularization, RNN, Robot rights, robotics, ROC (receiver operating characteristic) Curve, root directory, Root Mean Squared Error (RMSE), rotational invariance, sampling bias, SavedModel, Saver, scalar, scaling, scarcity of data scientists, scientific academic background, scikit-learn, scoring, second step, selection bias, semi-supervised learning, sensitive attribute, sequence model, services, serving, session (tf.session), shape (Tensor), shortened training, sigmoid function, similarity measure, simulations, single, size invariance, sketching, smart, social challenges, softmax, sparse feature, sparse representation, sparse vector, sparsity, sparsity/regularization (Ftrl), spatial pooling, specific language, sponsorship, squared hinge loss, squared loss, start a career in data science, State, state-action value function, state-of-the-art, static model, stationarity, steadily increasing, step, step size, stochastic gradient descent (SGD), stride, structural risk minimization (SRM), study advanced statistics, subsampling, success, successful data science career, successful opportunities, summary, supervised learning, supervised machine learning, supply chain management, support vector machines, sustainable, sustainable research-based programs, synthetic feature, systematic organization, Systems for ML, tabular Q-learning, target, target network, teach, technical presentations, TECHNOLOGY, temporal data, Tensor, Tensor Processing Unit (TPU), Tensor rank, Tensor shape, Tensor size, TensorBoard, TensorFlow, TensorFlow Playground, TensorFlow Serving, termination condition, test set, tf.Example, tf.keras, the amount of data, The conference, the data they own, The EU Framework Programme for Research and Innovation, The EU Framework Programme for Research and Innovation Horizon 2020, the EU’s blueprint, The European Research Area, The European Union, the field of data science, The Horizon 2020, The Horizon 2020 program, The Innovation Union, The Next Framework Programme, The secret to success is practice and perseverance, The Summit, Threat to human dignity, time series analysis, timestep, topics, tower, TPU, TPU chip, TPU device, TPU master, TPU node, TPU Pod, TPU resource, TPU slice, TPU type, TPU worker, training, training set, trajectory, transfer learning, translational invariance, trigram, true negative (TN), true positive (TP), true positive rate (TPR), tutorial, unawareness (to a sensitive attribute), underfitting, unlabeled example, unsupervised learning, unsupervised machine learning, update frequency, upweighting, user matrix, validation, validation set, value, vanishing gradient problem, Wasserstein loss, Wealth, Weaponization of AI, weight, Weighted Alternating Least Squares (WALS), wide model, width, will continue to grow, Work Programme, worth, worth the effort, write code
Related posts
CVPR 2020 • FATE in Computer Vision
CVPR 2020 • Learning 3D Generative Models Workshop
About The Author
sherry
CODESIGN.BLOG