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Evelyn Ding
Senior Machine Learning Engineer at Habana Labs
algorithm, api, box plot, cloud computing, control flow, deep learning, electric battery, flowchart, fox broadcasting company, hpo, hyperparameter (machine learning), hyperparameter optimization, implementation, interface (computing), loss function, machine learning, machine learning platform, machine learning tools, mathematical optimization, ml, mlperf, number, patch (computing), reason, recommendation systems, research, software , twitter, workload
Basem Barakat
Large Scale Machine Learning Engineer at Habana Labs
algorithm, api, box plot, cloud computing, control flow, deep learning, electric battery, flowchart, fox broadcasting company, hpo, hyperparameter (machine learning), hyperparameter optimization, implementation, interface (computing), loss function, machine learning, machine learning platform, machine learning tools, mathematical optimization, ml, mlperf, number, patch (computing), reason, recommendation systems, research, software , twitter, workload
David Austin
Sr Principal Engineer at Intel
accuracy and precision, attention, brute-force search, convolutional neural network, data science, equinor, experiment, first-order logic, hyperparameter (machine learning), hyperparameter optimization, image classification, image classifier, imagenet, insight, kaggle, kaggle grandmaster, learning rate, machine learning, machine learning platform, machine learning tools, mathematical optimization, memory, ml, outlier, overfitting, parameter, pixel, program optimization, satellite, source lines of code, visualization (graphics), workflow
Meghana Ravikumar
Machine Learning Engineer at SigOpt
accuracy and precision, attention, bullet, cluster analysis, distillation, engine, experiment, function (mathematics), future, hyperparameter optimization, ice, information, learning, machine learning, mathematical optimization, memory, metric (mathematics), parameter, pareto efficiency, question, reading comprehension, startup company, use case, water, wine
Kriti Kohli
Principal Data Scientist at IBM
analytics, bricks and clicks, consumer behaviour, consumption (economics), covid-19 pandemic, deep learning, dependent and independent variables, economics, forecasting, hyperlocal, hyperparameter optimization, index (economics), inventory, k-nearest neighbors algorithm, machine learning, merchandising, online shopping, overfitting, point of sale, prediction, retail, seasonality, shopping, time series
Adam Grzywaczewski
Senior Deep Learning Data Scientist at NVIDIA
algorithm, computational resource, computer graphics, computer vision, computing, deep learning, graphics processing unit, hyperparameter optimization, machine learning, natural language processing, neural network, nvidia, perception, reason, recommender system, self-driving car, supercomputer, supervised learning, theory, training, validation, and test sets, translation, unsupervised learning, visual perception
Assel Altayeva
Data Quality Lead, Strategy & Planning at Peabody
administrative data, adolescence, algorithm, autoregressive model, birth control, blockchain, caesarean section, calibration, causality, child development, childbirth, clinical trial, communication protocol, computer network, conversation, cryptocurrency, data science, differential privacy, economic inequality, encryption, epidemiology, ethereum, federated learning, function (mathematics), gaussian function, gestational age, health, hyperparameter optimization, hypothesis, information, information privacy, information security, life expectancy, likelihood function, likelihood-ratio test, liverpool, machine learning, maximum likelihood estimation, mean, mental health, model selection, mother, multimodal systems, multivariate normal distribution, national health service (england), neural network, normal distribution, parameter, poverty, predictive modelling, pregnancy, privacy, privacy-enhancing technologies, public housing in the united kingdom, reason, research, risk, science, sensitivity and specificity, server (computing), social work, society for worldwide interbank financial telecommunication, statistical causality, statistics, substance abuse, teenage pregnancy, time series, tropical medicine, variance, vector autoregression, violence, white noise
Anna Zaremba
Lead Data Scientist at Hunter Labs Tech
adjacency matrix, analogy, autism spectrum, autoregressive model, bit, blockchain, brain, calibration, causality, communication, computer vision, cryptocurrency, data science, deep learning, detecting fake news online, dimensionality reduction, ethereum, fake news, function (mathematics), gaussian function, gender, graph (discrete mathematics), hyperparameter optimization, hypothesis, image segmentation, information, integer programming, likelihood function, likelihood-ratio test, linear programming, mathematical optimization, matrix (mathematics), maximum likelihood estimation, model selection, multimodal systems, multivariate normal distribution, neuroscience, normal distribution, parameter, predictive modelling, research, science, semi-supervised learning, social network, statistical causality, time, time series, trace (linear algebra), vector autoregression, white noise
Sara Robinson
Developer Advocate at Google
#googleio, accuracy and precision, ai and google cloud, ai with google cloud, amazon web services, apache cassandra, api, application software, approximation, artificial neural network, automation, autoscaling, base64, batch processing, bias, bit, cash, cloud ai, cloud computing, command-line interface, computer data storage, computer programming, computer vision, computing, conceptual model, confusion matrix, continuous integration, credit card, data pre-processing, data warehouse, database, design, devops, emotion, end user, engineering, engineering design process, explainable artificial intelligence, feature engineering, firebase, flowchart, fraud, giphy, github, google announcement, google cloud ai, google conference. google, google developer conference, google i/o, google io, google search, governance, high availability, hyperparameter (machine learning), hyperparameter optimization, hypertext transfer protocol, innovation, integrated development environment, jetbrains, kubernetes , latency (engineering), linear regression, machine learning, machine learning with cloud ai, mathematical optimization, metadata, microservices, ml with cloud ai, neural network, online and offline, optical character recognition, perceptron, pipeline (computing), pipeline (software), pixel, porting, pr_pr: google i/o, prediction, privacy, project jupyter, public-key cryptography, purpose: educate, real-time computing, reason, regression analysis, regulatory compliance, sampling (statistics), scheme (programming language), sentiment analysis, shader, social media, software , software framework, software testing, software versioning, statistical classification, supervised learning, system, team, tensorflow, tensorflow 2021, training, validation, and test sets, transcription (linguistics), transparency (behavior), transport layer security, twitter, type: conference talk (full production), type: conference talk (full production);, upload, user interface, web application, website, wikipedia, word embedding, workflow, workload, world wide web
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
Swetha Revanur
Founding Engineer at Hebbia.AI
ai, artificial intelligence, computer science, convolutional neural network, data science conference 2020, data science tips, deep learning, diabetic retinopathy, education and research, healthcare, healthcare ai, hyperparameter optimization, intelligence, machine learning, magnetic resonance imaging, medical diagnosis, medical imaging, multiclass classification, natural language processing, pathology, radiology, retina, robotics, self-driving car, sensitivity and specificity, sensor, statistical classification, surveillance, training, validation, and test sets, turing test, understanding, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , women in data science, women in technology
Bhawna Nigam
Advisor at Deepiotics Pvt. Ltd.
ai, artificial intelligence, computer science, convolutional neural network, data science conference 2020, data science tips, deep learning, diabetic retinopathy, education and research, healthcare, healthcare ai, hyperparameter optimization, intelligence, machine learning, magnetic resonance imaging, medical diagnosis, medical imaging, multiclass classification, natural language processing, pathology, radiology, retina, robotics, self-driving car, sensitivity and specificity, sensor, statistical classification, surveillance, training, validation, and test sets, turing test, understanding, wids 2020, wids mumbai 2020, wids worldwide, woman in data science , women in data science, women in technology
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
Karthika Kamath
Decision Scientist / Imagineer at Fractal
effective data visualization in the era of covid-19, forecasting , accuracy and precision, activation function, algorithm, analytics, android (operating system), anns, artificial intelligence, artificial neural network, artificial neural networks, autoregressive integrated moving average, basic reproduction number, bias, bioinformatics, blog, business-to-business, cartesian coordinate system, coefficient of determination, communication, complex analysis, computer, computer programming, computer vision, 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 and financial services, data science conference 2020, data science tips, data science tutorial, data visualization , decision-making, deep learning, dependent and independent variables, derivative, differential equation, dimension, economics, education and research, entrepreneurship, equation, errors and residuals, estimator, expert, feeling, force, forecasting, function (mathematics), future, fuzzy logic, genie (feral child), go (programming language), hyperparameter (machine learning), hyperparameter optimization, initial condition, input/output, instagram, internship, knowledge, learning, linear regression, machine learning, mathematical model, mathematical optimization, maxima and minima, measurement, median, mentorship, moving-average model, mumbai 2020, music, natural language processing, natural-language generation, natural-language understanding, neural networks, neural networks 2020, neural networks and deep learning, neural networks machine learning , neural networks tutorial, nonlinear system, normal distribution, ordinary differential equation, parameter, partial derivative, perception, podcast, prediction, programming language, random walk, reddit, regression analysis, research, retail, seasonality, sine, snns, space, spotify, standard deviation, stationary process, statistics, strategic management, time, time series, twitter, uber, uncertainty, use case, variance, video, visualization (graphics), volatility (finance), weather, wids 2020, wids ambassadors, wids mumbai 2020, wids worldwide, woman in data science , woman in data science mmmbai , women in ai, women in analytics, women in ds, women in programming
Cees Snoek
Professor in Artificial Intelligence at University of Amsterdam
algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance
Kuan-Chuan Peng
Research Scientist at Mitsubishi Electric Research Laboratories
algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance
Andrew David Bagdanov
Associate Professor at University of Florence
algorithmic bias, artificial neural network, central processing unit, computer vision, concept, conceptual model, convolutional neural network, data sheet, deep learning, feature learning, hyperparameter optimization, image quality, image segmentation, imagenet, learning to rank, neural network, overfitting, prediction, programmer, representation (arts), statistical classification, supervised learning, support-vector machine, unsupervised learning, variance
Ruchika Chavhan
Researcher at Indian Institute of Technology Mumbai
accuracy and precision, ada-at/dt, architecture, attention, data, devon, equation, experiment, france, function (mathematics), hyperparameter optimization, image segmentation, inference, information, knowledge, loss function, market segmentation, mathematical optimization, methodology, minimax, pain, paper, prediction, training, validation, and test sets, transfer function
Yonggang Hu
Distinguished Engineer and Chief Architect at IBM
accuracy and precision, adaptation, artificial intelligence, artificial neural network, continuous uniform distribution, cross-validation (statistics), data science, database, deep learning, dimension, discrete logarithm, engineering, experiment, file system, food, graphics processing unit, horse, http cookie, hyperparameter (machine learning), hyperparameter optimization, information, iphone x, knowledge, learning, library, machine learning, mathematical optimization, mobile app, music, orbital hybridisation, parameter, patch (computing), privacy, probability distribution, productivity, python, reinforcement learning, research, science, science research, scientific computing, scipy2021, siri, software bug, software engineering, space, statistics, temperature, toyota, truth, user interface, venmo, version control
Xavier Bouthillier
PhD Student & Research Developer at Mila, Université de Montréal
artificial intelligence, continuous uniform distribution, cross-validation (statistics), data science, database, deep learning, dimension, discrete logarithm, engineering, file system, hyperparameter optimization, information, iphone x, learning, library, machine learning, mathematical optimization, music, orbital hybridisation, privacy, probability distribution, python, research, science, science research, scientific computing, scipy2021, software bug, software engineering, temperature, truth, version control
Stephen Bailey
Physicist Project Scientist at Lawrence Berkeley National Laboratory
artificial intelligence, astronomy , central processing unit, charge-coupled device, correlation and dependence, cuda, dark energy, data science, diffraction, engineering, graphics processing unit, hyperparameter optimization, kernel (operating system), machine learning, message passing interface, multi-core processor, numpy, nvidia, optical spectrometer, parallel computing, physical cosmology, point spread function, profiling (computer programming), python, python 2021, python tutorial, python virtual conference, quasar, science, science research, scipy2021, software engineering, supercomputer, surveying, telescope, unit testing
Daniel Margala
Data Scientist at Fitbit
artificial intelligence, astronomy , central processing unit, charge-coupled device, correlation and dependence, cuda, dark energy, data science, diffraction, engineering, graphics processing unit, hyperparameter optimization, kernel (operating system), machine learning, message passing interface, multi-core processor, numpy, nvidia, optical spectrometer, parallel computing, physical cosmology, point spread function, profiling (computer programming), python, python 2021, python tutorial, python virtual conference, quasar, science, science research, scipy2021, software engineering, supercomputer, surveying, telescope, unit testing
Laurie Stephey
Data Analytics Engineer at NERSC
algorithms, analytics, array data structure, artificial intelligence, astronomy , automatic vectorization, batch processing, business intelligence, central processing unit, charge-coupled device, compiler, compilers, computational science, computer cluster, computer science, computing, correlation and dependence, cray, cuda, customs, dark energy, dark energy spectroscopic instrument, dashboard (business), data analytics, data science, database, diffraction, elasticsearch, engineering, graphics processing unit, human-readable medium, hyperparameter optimization, inspection, intel, interpreter (computing), just-in-time compilation, kernel (operating system), kubernetes , llvm, machine learning, machine programming, message passing interface, metadata, modeling, modular programming, multi-core processor, numpy, nvidia, optical spectrometer, parallel computing, physical cosmology, point spread function, profiling (computer programming), program optimization, project jupyter, prototype, python, python (programming language), python 2021, python tutorial, python virtual conference, quasar, reason, research, science, science research, scipy, scipy2021, scripting language, simulation, software , software engineering, software tools, spectroscopic data, statistics, supercomputer, surveying, system, telescope, theory, type system, understanding, unit testing, variable (computer science), visualization (graphics), web browser
Rollin Thomas
Big Data Architect at Berkeley Lab
algorithms, analytics, artificial intelligence, astronomy , batch processing, business intelligence, central processing unit, charge-coupled device, compilers, computational science, computer cluster, computer science, computing, correlation and dependence, cray, cuda, customs, dark energy, dashboard (business), data analytics, data science, database, diffraction, elasticsearch, engineering, graphics processing unit, hyperparameter optimization, inspection, kernel (operating system), kubernetes , machine learning, machine programming, message passing interface, metadata, modeling, multi-core processor, numpy, nvidia, optical spectrometer, parallel computing, physical cosmology, point spread function, profiling (computer programming), project jupyter, prototype, python, python (programming language), python 2021, python tutorial, python virtual conference, quasar, research, science, science research, scipy2021, software , software engineering, software tools, statistics, supercomputer, surveying, telescope, unit testing, visualization (graphics)
Viswesh Periyasamy
Software Engineer at Databricks
apache spark, brute-force search, central processing unit, cluster analysis, clustered file system, computational complexity theory, computational phylogenetics, computer network, cross-validation (statistics), data-intensive computing, databricks, deep learning, expert, hyperparameter optimization, instruction pipelining, k-means clustering, library (computing), loss function, machine learning, mathematical optimization, parallel computing, principal component analysis, program optimization, random forest, source code, subroutine
Chunxiang (Jake) Zheng
Software Engineer at Google
analytics, cloud computing, computer, computer hardware, computer network, computing, data center, database, differential privacy, emoji, federated learning, gboard, grid computing, hyperparameter optimization, internet of things, internet privacy, keyboard technology, machine learning, mobile app, privacy, proxy server, server (computing), smartphone, tensorflow, wi-fi
Emily Glanz
Software Engineer at Google
analytics, cloud computing, computer, computer hardware, computer network, computing, data center, database, differential privacy, emoji, federated learning, gboard, grid computing, hyperparameter optimization, internet of things, internet privacy, keyboard technology, machine learning, mobile app, privacy, proxy server, server (computing), smartphone, tensorflow, wi-fi
Liang Zhang
Global Sales Strategy & Operations at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, apache spark, api, application software, applications at linkedin, array data structure, array data type, art, artificial intelligence tutorial, artificial neural network, assembly language, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), color, common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data conversion, data science tutorial, data type, debugging, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , file system, film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, library (computing), likelihood function, linear combination, linearity, linkedin, literature, loader (computing), logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parameter (computer programming), parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, preprocessor, protein–protein interaction, python (programming language), radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , source lines of code, space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, string (computer science), subroutine, summation, supervised learning, support-vector machine, temporal topic localization, tensor, texas, three-dimensional space, training, validation, and test sets, transcription (linguistics), transformer (machine learning model), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Di Wen
Staff Software Engineer at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, ads recommendation, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, click-through rate, closed captioning, code-division multiple access, codec, coherence (physics), common technologies for feed and ads recommendation, compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, cosine similarity, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, feed recommendation , film frame, finite set, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, image understanding, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, introduction - feed, ads, search and spam, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, letter case, likelihood function, linear combination, linearity, linkedin, literature, logic, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimedia search, multimodality, multivariate random variable, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, simulation, sms, social media, social networking service, softmax function, software , space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, summation, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video representations used in production, video search, video search engine, video understanding, weight, youtube
Yijie Dylan Wang
Software Engineering Manager at LinkedIn
before deep learning , artificial intelligence , artificial intelligence tutorial, computer science conference , feed and ads modeling, introduction to multimedia, visio-lingual representations, 3d cnns, accent (sociolinguistics), accuracy and precision, acm 2020 tutorial, acoustic model, action recognition, advertising, algorithm, angle, apache hadoop, api, application software, applications at linkedin, art, artificial intelligence tutorial, artificial neural network, association for computing machinery, attention, attention (machine learning), average, baby talk, bag-of-words model, bangalore, beef, before deep learning , bible, bichon frise, cartesian coordinate system, castle, central processing unit, closed captioning, code-division multiple access, codec, coherence (physics), compass, computer, computer graphics, computer performance, computer science tutorial, computer science tutorials, computer vision, computer vision 2020, computing, concept, convolution, convolutional neural network, data, data compression, data science tutorial, deconvolution, deep learning, deep learning and cnns, deep learnning, deep learnning tutorial, definition, depiction, design, desk, diagram, diamond, digital image processing, dimension, dsgmm and deep cluster-and-aggregate method, dynamic programming, education, email spam, energy, engine, enzyme kinetics, equation, essay, euclidean vector, experiment, extract, transform, load, feature (machine learning), feature extraction, feature learning, film frame, function (mathematics), gas, gender, gradient, grayscale, hidden markov model, histogram, history, hyperparameter optimization, image embeddings, image representations , image segmentation, imagenet, improvements on 3d cnn, improvements on 3d cnns and two-stream, infection, information, information retrieval, input/output, inspection, instagram, intelligence, interface (computing), internet, inverted index, k-means clustering, kdd2020 tutorials, language, language model, learning, lecture-style tutorials, likelihood function, linearity, linkedin, literature, long short-term memory, machine, machine learning, map, markov chain, markov model, mathematical optimization, matrix (mathematics), mean, meme, memory, metric learning for images, metric space, microsoft, mixture model, mobile app, monotonic function, motivation, moving average, mp3, multimedia, multimedia infrastructure, multimodality, music, nature, navigation, neural network, news, non-local networks and slowfast, nothing, number, object detection, online and offline, optical character recognition, optical flow, optimization for cnns, oracle corporation, parameter, parity bit, phoneme, pixel, plasterwork, podcast, precision and recall, prediction, protein–protein interaction, radio, reason, recommender system, recurrent neural network, research, robust statistics, satya nadella, search algorithm, search engine, search engine indexing, self-supervised learning, self-supervised video embeddings, sense, signal, similarity learning, sms, social networking service, softmax function, space, spam detection, spamming, spectral density, spectrogram, speech, speech recognition, speech technologies for video understanding, spotify, statistical classification, statistics, streaming media, supervised learning, support-vector machine, temporal topic localization, texas, three-dimensional space, transcription (linguistics), transmission (mechanics), truck, two-stream networks, typing, understanding, unsupervised learning, upload, use case, variance, vector graphics, vector space, video, video captioning, video classification, video embeddings and networks, video game, video game console, video game live streaming, video search, video search engine, video understanding, weight, youtube
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