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Rafael Gomez-Bombarelli
Professor at MIT
algorithm, astronaut training, atom, atomistic simulation, autoencoder, big data, bit, cell (biology), chemical formula, chirality, computer, continuum mechanics, data, deep learning, energy, entropy, experiment, formula, geometry, gradient descent, information, isomer, knowledge, learning, machine learning, machine learning platform, machine learning tools, materials design, materials science, memory, message passing, ml, molecule, prediction, protein, prototype, reality, reason, science, simulation, siri, solution, space, stack (abstract data type), statistics, technology, temperature, time, truth, variational autoencoder
Scott Clark
Co-founder and CEO at SigOpt
accuracy and precision, ai, algorithm, alphago, analytics, api, artificial neural network, autism, black box, brute-force search, chatbot, cluster analysis, computer vision, deep learning, engineering, experiment, functional magnetic resonance imaging, gradient descent, hpo, hyperparameter (machine learning), hyperparameter optimization, intelligence, intelligent experimentation, internet bot, learning, machine learning, machine learning platform, machine learning tools, machinelearning, magnetic resonance imaging, mathematical optimization, mathematics, memory, ml, natural language processing, neural network, precision medicine, reinforcement learning, research, scientific method, simulation, slack (software), software framework, space, stochastic gradient descent, tensorflow, web 2.0
Davide Venturelli
Artificial Intelligence, Quantum Computing, Physics, Robotics, Entrepreneurship at USRA
algorithm, application software, bayesian inference, brute-force search, cluster analysis, computing, database index, estimation theory, gradient descent, likelihood function, linear programming, machine learning, mathematical optimization, matrix (mathematics), maximum a posteriori estimation, maximum likelihood estimation, mean, quantum computing, quantum superposition, sampling (statistics), software , statistical inference, statistics, support-vector machine, zero-sum game
Alexey Gorshkov
Physicist at NIST
algorithm, behavior, coefficient, computer, energy, function (mathematics), gradient descent, ground state, hamiltonian (quantum mechanics), indoor cycling, linear combination, mathematical optimization, maximum cut, oak, optimization problem, paper, quantum computing, quantum supremacy, reason, simulation, space, spin (physics), triangle, truth, weight
Josh Wills
Developer Without Affiliation
apache hadoop, autocomplete, backup, cache (computing), categorization, central processing unit, compartmental models in epidemiology, computer data storage, computer programming, database, deep learning, education, elasticsearch, electronic health record, email, epidemiology, ethics, feedback, github, gradient descent, hard coding, hyperparameter (machine learning), influenza, influenza pandemic, information retrieval, instruction pipelining, learning to rank, logistic regression, lyft, machine learning, mapreduce, markov chain, markov chain monte carlo, mean, median, metropolis–hastings algorithm, mind, negative-feedback amplifier, neural network, nosql, open source, organization, parallel computing, parameter, prediction, programming language, public health, python (programming language), random forest, random-access memory, reddit, replication (computing), retail, search engine indexing, search engine results page, severe acute respiratory syndrome coronavirus 2, slack (software), software , software bug, software development, source code, spanish flu, statistical hypothesis testing, structure, textbook, time, twitter, uncertainty, understanding, user-generated content, version control, web application, wikipedia, world wide web
Zhiqiang Xu
Senior Software Development Engineer at Microsoft
galveston, texas, gear, gradient, gradient descent, huawei, information, israel, luck, map, matrix (mathematics), mexico, mirror, panama, radiator, retraction (topology), romania, sauce, space, spain, stochastic gradient descent, subspace topology, tomato, united states, universe, yellow ribbon
Brujo Benavides
Staff Engineer at NextRoll
30 rock, 3d printing, acura, airline, amelia island, android (operating system), anomaly detection, anonymous function, apple inc., application software, aquarium, aurora (disney), ballistics, barometer, behavior, binary file, bit, blog, boolean data type, brown dwarf, california, callback (computer programming), canada, cell site, central processing unit, cisco systems, classical element, classical guitar, code, colombia, color, command-line interface, communication, compiler, computer, computer data storage, computer hardware, computer network, computer programming, computing, confidentiality, covid-19 pandemic, cut, copy, and paste, database, dell, dental braces, directory (computing), dog, ebay, elijah, elmo, elvis presley, email, emoji, employment, encapsulation (computer programming), error, espn, feedback, feeling, floating-point arithmetic, food, function (mathematics), functional programming, glasses, google, gradient descent, graphics processing unit, guitar, hippie, hispanic and latino americans, http cookie, information, information retrieval, interface (computing), internet, internet of things, interview, intrusion detection system, ios, java (programming language), knowledge, language, lawyer, learning curve, lease, library, library (computing), list comprehension, logic, love, luck, machine, machine learning, magic (supernatural), makefile, mars, mars landing, mathematical optimization, mathematics, meme, memory, metadata, metallica, method (computer programming), mobile app, modular programming, mother, motivation, music, navigation, neural network, news, nothing, nvidia, observation, open source, open-source-software movement, oregon, paragraph, parameter (computer programming), perception, perseverance (rover), philadelphia, philippines, playstation, playstation vita, price, property, random-access memory, raspberry pi, real-time computing, reason, recursion, recursion (computer science), reddit, reliability engineering, research, return statement, rock music, ruby (programming language), runtime system, samsung galaxy s4, satellite, sauce, semicolon, server (computing), sibling, siri, software , source code, source lines of code, space, spain, spanish language, speech synthesis, statistics, string (computer science), stroke, stylus, subroutine, text messaging, the home depot, the runaways (2010 film), time, timer, truth, twitter, type system, united states, vector processor, version control, video card, visual basic, wind, youtube
Guy Royse
Developer Advocate at Redis
activation function, artificial neural network, backpropagation, bit, daphne blake, deep learning, dimension, four-dimensional space, gradient descent, machine learning, memory, recurrent neural network, scoob!, scooby-doo, scooby-doo (character), scooby-doo! mystery incorporated, sentiment analysis, shaggy rogers, six feet up, sixfeetup, slope, softmax function, training, validation, and test sets, two-dimensional space, vanishing gradient problem, velma dinkley
Ekta Khanna
Staff Engineer at VMware
algorithm, analytics, api, artificial neural network, computer, computer vision, convolutional neural network, database, deep learning, digital image processing, experiment, gradient descent, graphics processing unit, hyperparameter optimization, library (computing), machine learning, mathematical optimization, memory, neural networks, parallel computing, regularization (mathematics), roman empire, smartphone, speech recognition, tensorflow
Kirti Khade
Associate Consultant at Servian
activation function, analytics, artificial intelligence 2020, data science, data analysis, data science, deep learning, education and research, evaluation, gradient descent, information, language, learning rate, luck, machine learning, matrix (mathematics), neural network, neural networks and deep learning, neural networks machine learning , neural networks tutorial, perception, prediction, sales, science, statistics, supervised learning, supply chain, switch, technology, training, validation, and test sets, web browser, wids mumbai 2020, wids worldwide, woman in data science
Julian Sara Joseph
Senior Full Stack Engineer at Somnoware Healthcare Systems
effective data visualization in the era of covid-19, ai, algorithm, api, artificial intelligence, basic reproduction number, big data, blog, blood donation, blood transfusion, bootstrapping (statistics), constrained optimization, convex function, covid-19, covid-19 data visualization, covid-19 data analysis, covid-19 modelling, covid-19 visualization, data analysis, data science, data science 2020, data science conference 2020, data science in social media, decision tree learning, deep learning, dependent and independent variables, derivative, determinism, differential equation, equation, errors and residuals, estimator, facebook, function (mathematics), gender, genie (feral child), google search, gradient descent, hashtag, hessian matrix, hyperparameter (machine learning), hyperparameter optimization, initial condition, instagram, lasso (statistics), learning, likelihood function, livestreaming, logistic regression, loss function, machine learning, mathematical model, mathematical optimization, music, neural network, news, odds ratio, ordinary differential equation, overfitting, parameter, podcast, privacy, random forest, regression analysis, research, sentiment analysis, sexual orientation, sine, social media, social media analytics, social media analytics python , social media data analytics, social media data analytics tools, social media data analytics tutorial, social media data science, social network, social network analysis, spotify, statistical classification, statistics, supervised learning, twitter, variance, video, whatsapp, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , woman in data science mmmbai , women in ds, women in tech, women in technology, youtube
Madhavi Kaivalya Kandalam
Chief Data Scientist at Loylty Rewards
ai, artificial intelligence, bootstrapping (statistics), constrained optimization, convex function, data science, data science conference 2020, decision tree learning, deep learning, derivative, determinism, errors and residuals, gradient descent, hessian matrix, lasso (statistics), likelihood function, logistic regression, loss function, machine learning, mathematical optimization, neural network, odds ratio, overfitting, random forest, regression analysis, statistical classification, statistics, supervised learning, variance, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , women in tech, women in technology
Abishai Ebenezer
Student at Microsoft Learn Ambassador
ant colony optimization algorithms, artificial neural network, atom, backpropagation, bio-inspired computing, brain, calculus, deep learning, dendrite, experiment, gradient descent, information, intuition, machine learning, mathematical model, mathematical optimization, mathematics, neural network, neuron, overfitting, particle swarm optimization, perception, statistical classification, swarm behaviour
Foteini Savvidou
Electrical & Computer Engineering Student at Microsoft Learn Student Ambassador
automated machine learning, big data, bit, coefficient of determination, correlation and dependence, design, experience, experiment, file explorer, gradient descent, histogram, interface (computing), lady death, learning, library, machine learning, matrix (mathematics), outlier, prediction, real-time computing, regression analysis, research, skype, statistics, vibration
Stephanie Hyland
PhD student at Machine Learning & Computational Biology Lab at ETH Zurich
algorithm, bit, database, determinism, differential privacy, equation, gradient descent, health care, logistic regression, loss function, machine learning, mathematical optimization, mean, neural network, nonlinear system, randomness, regression analysis, sampling (statistics), standard deviation, statistical classification, statistics, stochastic differential equation, stochastic gradient descent, variance
David Kanter
Vice President at MLCommons
artificial neural network, benchmarking, best practice, big data, computer vision, convolutional neural network, fugaku (supercomputer), gradient descent, imagenet, innovation, kubernetes , machine learning, natural language processing, neural network, open data, platform, research, scientific method, self-driving car, software testing, speech recognition, stochastic gradient descent, supercomputer, visual impairment
Mei Si
Associate Professor at Rensselaer Polytechnic Institute
algorithm, artificial intelligence, artificial neural network, bit, blackjack, carpool, computing, data, games, gradient, gradient descent, graph (discrete mathematics), institute of technology, learning, machine learning, parameter, quantum circuit, quantum computing, quantum entanglement, quantum mechanics, quantum superposition, qubit, reinforcement, reinforcement learning, reward system, signal, wisconsin
Laurent Charlin
Assistant Professor at HEC Montreal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Antoine Prouvost
PhD Student at Polytechnique Montreal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Giulia Zarpellon
PhD Student at Polytechnique Montréal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Elias B. Khalil
Assistant Professor at University of Toronto
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Maxime Gasse
Researcher at Polytechnique Montréal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Bistra Dilkina
Assistant Professor at University of Southern California
approximation algorithm, artificial intelligence, artificial neural network, atom, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), constraint (mathematics), control flow, control theory, data, door, equation, feasible region, food, function (mathematics), games, generation, gradient descent, graph (discrete mathematics), greedy algorithm, hyperparameter optimization, information, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, map, neural network, neuron, procedural programming, protein, reinforcement learning, routing, search algorithm, signal, space, tile, time complexity, training, validation, and test sets, travelling salesman problem, variable (mathematics), vehicle routing problem, vertex cover, video, video game
Didier Chetelat
Researcher at Polytechnique Montréal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Andrea Lodi
Researcher at Polytechnique Montréal
approximation algorithm, best, worst and average case, brute-force search, centrality, combinatorial optimization, component (graph theory), equation, feasible region, gradient descent, greedy algorithm, hyperparameter optimization, integer programming, karush–kuhn–tucker conditions, learning to rank, linear programming, linear programming relaxation, machine learning, neural network, reinforcement learning, routing, search algorithm, time complexity, travelling salesman problem, vehicle routing problem, vertex cover
Hugo Larochelle
Research Scientist at Google
computer science event 2020, artificial neural network, computer science 2020, concept, cross entropy, data mining, deep learning, deep learning 2020, dependent and independent variables, distance, expert, gradient descent, hugo larochelle, imagenet, kdd2020, learning, machine learning, overfitting, prediction, prime number, softmax function, statistical classification, subroutine, test (assessment), training, training, validation, and test sets, transfer learning, understanding
Viral B. Shah
Co-Founder and CEO at Julia Computing Inc.
ai, ai for engineers, algebra, artificial intelligence, artificial neural network, automatic differentiation, backpropagation, business related problems, c (programming language), c++, cloud computing, compiler, complex number, complexity, computer vision, computing, control flow, cuda, data science, deep learning, differentiable programming, dimension, distributed computing, function (mathematics), gradient descent, graphics processing unit, hessian matrix, input/output, internet, interoperability, inverse problem, julia, julia (programming language), library (computing), linear programming, luck, machine learning, mathematical optimization, matlab, number, numerical linear algebra, odsc, open data science conference, parallel computing, polynomial, prediction, problem solving, program optimization, programming language, python, quadratic programming, reason, scenario planning, scientific modelling, self-driving car, simulation, software , statistics, supercomputer, systems biology, tensorflow, theory, thread (computing)
Ganapathi Pulipaka
Chief Data Scientist at Accenture
ai, alphago, approximation, arm architecture, artificial general intelligence, artificial intelligence, artificial neural network, automation, blockchain, bold360, brain, business ai integration, central processing unit, cloud computing, computer programming, computing, conversational ai, crowdsourcing, data science, data scientist, deep learning, deepmind, events, exoplanet, extraterrestrial life, forecasting, function (mathematics), generative adversarial network, gradient descent, innovation, lawrence livermore national laboratory, logarithm, logmein, machine learning, mathematical optimization, memory, nervous system, neural network, nuclear fusion, policy, prediction, pytorch, recommender system, reinforcement, reinforcement learning, robotics, self-driving car, simulation, smart city, space exploration, statistical classification, supercomputer, supervised learning, technological singularity, tensorflow, therapy, trajectory, transform 2019, universe, unsupervised learning, vector processor, venturebeat
Josh Mineroff
Field Engineer at Domino Data Lab
deep learning in practice, tutorial pycon us 2021, artificial neural network, automatic differentiation, backpropagation, brain, computer vision, convolutional neural network, cross entropy, deep learning, deep learning tuturial, evaluating models, facial recognition system, for loop, gradient descent, hyperparameter (machine learning), introduction, learning rate, loss function, machine learning, matrix (mathematics), mean squared error, multilayer perceptron, optimizing models, perceptron, pycon 2021 tutorials, pycon us, pycon us 2021, python, python 2021, python community, python tips and tricks, python tutorials 2021, recurrent neural network, regularization (mathematics), self-driving car, subroutine, summary, vehicular automation, virtual assistant
Andrea Lowe
Customer Success Engineer at Domino Data Lab
deep learning in practice, tutorial pycon us 2021, artificial neural network, automatic differentiation, backpropagation, brain, computer vision, convolutional neural network, cross entropy, deep learning, deep learning tuturial, evaluating models, facial recognition system, for loop, gradient descent, hyperparameter (machine learning), introduction, learning rate, loss function, machine learning, matrix (mathematics), mean squared error, multilayer perceptron, optimizing models, perceptron, pycon 2021 tutorials, pycon us, pycon us 2021, python, python 2021, python community, python tips and tricks, python tutorials 2021, recurrent neural network, regularization (mathematics), self-driving car, subroutine, summary, vehicular automation, virtual assistant
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