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Description: Agendas of past and upcoming iterations of the DataConf data science conference and related resources. - DataHackIL/DataConf

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DataConf 2016https://github.com/DataHackIL/DataConf#dataconf-2016
Prof. Shai Shalev Shwartz, The Hebrew University of Jerusalem - Reinforcement Learninghttps://github.com/DataHackIL/DataConf#prof-shai-shalev-shwartz-the-hebrew-university-of-jerusalem-reinforcement-learning
Shahar Weinstock, Intel - Customers discovery using machine learninghttps://github.com/DataHackIL/DataConf#shahar-weinstock-intel-customers-discovery-using-machine-learning
Adi Nesher, PayPal - Defining the right label: How to create a valuable population tag in situations of uncertaintyhttps://github.com/DataHackIL/DataConf#adi-nesher-paypal-defining-the-right-label-how-to-create-a-valuable-population-tag-in-situations-of-uncertainty
Assaf Feldman, CTO, Riskified - Feature engineering at scalehttps://github.com/DataHackIL/DataConf#assaf-feldman-cto-riskified-feature-engineering-at-scale
Olha Shainoha, Wix - Site topic classification at Wixhttps://github.com/DataHackIL/DataConf#olha-shainoha-wix-site-topic-classification-at-wix
Amalia Bryl & Shahar Wilner, EDvantage - Can machine learning empower education?https://github.com/DataHackIL/DataConf#amalia-bryl-shahar-wilner-edvantage-can-machine-learning-empower-education
Dr. Yoram Gdalyahu, VP algorithms, Mobileye - Autonomous Driving and Crowd Mappinghttps://github.com/DataHackIL/DataConf#dr-yoram-gdalyahu-vp-algorithms-mobileye-autonomous-driving-and-crowd-mapping
Guy Adini, Istra - Toy Models for Algorithmic Tradinghttps://github.com/DataHackIL/DataConf#guy-adini-istra-toy-models-for-algorithmic-trading
DataConf 2017https://github.com/DataHackIL/DataConf#dataconf-2017
Yakov Shambik, Mobileye - Eye of the Beholder: Object detection in Mobileye using Deep Neural Networks and other techniqueshttps://github.com/DataHackIL/DataConf#yakov-shambik-mobileye-eye-of-the-beholder-object-detection-in-mobileye-using-deep-neural-networks-and-other-techniques
Ofer Ron, LivePerson - Concepts before machinery: Harnessing the power of domain expertise for machine-learning-based solutionshttps://github.com/DataHackIL/DataConf#ofer-ron-liveperson-concepts-before-machinery-harnessing-the-power-of-domain-expertise-for-machine-learning-based-solutions
Alex Ran, Intuit - Using Data Science for Automated Accountinghttps://github.com/DataHackIL/DataConf#alex-ran-intuit-using-data-science-for-automated-accounting
Meir Maor, SparkBeyond - Developing Simple and Stable Machine Learning Modelshttps://github.com/DataHackIL/DataConf#meir-maor-sparkbeyond-developing-simple-and-stable-machine-learning-models
Roii Spoliansky, PayPal - Active learning optimization as a function of label cost and mistake costhttps://github.com/DataHackIL/DataConf#roii-spoliansky-paypal-active-learning-optimization-as-a-function-of-label-cost-and-mistake-cost
Gil Chamiel, Taboola - Don’t believe everything your network tells you: Uncertainty in deep learning for recommender systemshttps://github.com/DataHackIL/DataConf#gil-chamiel-taboola-dont-believe-everything-your-network-tells-you-uncertainty-in-deep-learning-for-recommender-systems
Adina Lederhendler, Neura - General vs. subpopulation-specific modeling: When and why you need to get specifichttps://github.com/DataHackIL/DataConf#adina-lederhendler-neura-general-vs-subpopulation-specific-modeling-when-and-why-you-need-to-get-specific
Yonatan Wexler, Orcam - Fast and Furious Face Recognition: Efficient metric learning for video stream datahttps://github.com/DataHackIL/DataConf#yonatan-wexler-orcam-fast-and-furious-face-recognition-efficient-metric-learning-for-video-stream-data
Itamar Ben-Ari, Research Scientist @ Intel - Differentiable Memory Allocation Mechanism For Neural Computinghttps://github.com/DataHackIL/DataConf#itamar-ben-ari-research-scientist-intel-differentiable-memory-allocation-mechanism-for-neural-computing
Dr. Oshri, Rafael - Multi-agent deep reinforcement learning in communication networkshttps://github.com/DataHackIL/DataConf#dr-oshri-rafael-multi-agent-deep-reinforcement-learning-in-communication-networks
DataConf 2018https://github.com/DataHackIL/DataConf#dataconf-2018
Dana Kaner, Perimter X - Bootstrap, Random Forest and ll sorts of magichttps://github.com/DataHackIL/DataConf#dana-kaner-perimter-x-bootstrap-random-forest-and-ll-sorts-of-magic
Pavel Levin, Booking.com - Where should I travel next? Modeling multi-destination trips with Recurrent Neural Networks.https://github.com/DataHackIL/DataConf#pavel-levin-booking-com-where-should-i-travel-next-modeling-multi-destination-trips-with-recurrent-neural-networks
Ari Bornstien, Microsoft - Beyond Word Embeddingshttps://github.com/DataHackIL/DataConf#ari-bornstien-microsoft-beyond-word-embeddings
Dr. Michal Shmueli-Scheuer, IBM Research - Conversational bots for customer supporthttps://github.com/DataHackIL/DataConf#dr-michal-shmueli-scheuer-ibm-research-conversational-bots-for-customer-support
Nofar Betzalel, Paypal - Semi-Supervised Learning for Tagging Coverage Extensionhttps://github.com/DataHackIL/DataConf#nofar-betzalel-paypal-semi-supervised-learning-for-tagging-coverage-extension
Dr. Lev Faivishevsky, Intel Advanced Analytics - Using Deep-Learning to Detect Video distortionshttps://github.com/DataHackIL/DataConf#dr-lev-faivishevsky-intel-advanced-analytics-using-deep-learning-to-detect-video-distortions
Prof. Danny Pfeffermann, Central Bureau for Statistics - Can Big Data Really Replace Traditional Surveys for theProduction of Official Statistics?https://github.com/DataHackIL/DataConf#prof-danny-pfeffermann-central-bureau-for-statistics-can-big-data-really-replace-traditional-surveys-for-theproduction-of-official-statistics
Avi Hendler-Bloom, MobilEye - Overcoming the Electronic Traffic Sign Problemhttps://github.com/DataHackIL/DataConf#avi-hendler-bloom-mobileye-overcoming-the-electronic-traffic-sign-problem
Daniel Benzaquen, Lightricks - AB testing at Scalehttps://github.com/DataHackIL/DataConf#daniel-benzaquen-lightricks-ab-testing-at-scale
Gil Chamiel, Taboola - Deep And Shallow Learning in Recommendation Systemshttps://github.com/DataHackIL/DataConf#gil-chamiel-taboola-deep-and-shallow-learning-in-recommendation-systems
Oren Shamir, Innoviz Technologies - Neural networks for point clouds: adding the 3rd dimensionhttps://github.com/DataHackIL/DataConf#oren-shamir-innoviz-technologies-neural-networks-for-point-clouds-adding-the-3rd-dimension
DataConf 2016https://github.com/DataHackIL/DataConf#id4
https://github.com/DataHackIL/DataConf#dataconf-2016
https://www.facebook.com/events/555816034629213https://www.facebook.com/events/555816034629213
https://www.meetup.com/preview/DataHack/events/234096133https://www.meetup.com/preview/DataHack/events/234096133
Prof. Shai Shalev Shwartz, The Hebrew University of Jerusalem - Reinforcement Learninghttps://github.com/DataHackIL/DataConf#id5
https://github.com/DataHackIL/DataConf#prof-shai-shalev-shwartz-the-hebrew-university-of-jerusalem---reinforcement-learning
Shahar Weinstock, Intel - Customers discovery using machine learninghttps://github.com/DataHackIL/DataConf#id6
https://github.com/DataHackIL/DataConf#shahar-weinstock-intel---customers-discovery-using-machine-learning
Adi Nesher, PayPal - Defining the right label: How to create a valuable population tag in situations of uncertaintyhttps://github.com/DataHackIL/DataConf#id7
https://github.com/DataHackIL/DataConf#adi-nesher-paypal---defining-the-right-label-how-to-create-a-valuable-population-tag-in-situations-of-uncertainty
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2016/Smart_Labeling_of_Machine_Learning_Problems.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2016/Smart_Labeling_of_Machine_Learning_Problems.pdf
Assaf Feldman, CTO, Riskified - Feature engineering at scalehttps://github.com/DataHackIL/DataConf#id8
https://github.com/DataHackIL/DataConf#assaf-feldman-cto-riskified---feature-engineering-at-scale
Olha Shainoha, Wix - Site topic classification at Wixhttps://github.com/DataHackIL/DataConf#id9
https://github.com/DataHackIL/DataConf#olha-shainoha-wix---site-topic-classification-at-wix
Amalia Bryl & Shahar Wilner, EDvantage - Can machine learning empower education?https://github.com/DataHackIL/DataConf#id10
https://github.com/DataHackIL/DataConf#amalia-bryl--shahar-wilner-edvantage---can-machine-learning-empower-education
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2016/Edvantage-Machine_learning_and_edtech.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2016/Edvantage-Machine_learning_and_edtech.pdf
Dr. Yoram Gdalyahu, VP algorithms, Mobileye - Autonomous Driving and Crowd Mappinghttps://github.com/DataHackIL/DataConf#id11
https://github.com/DataHackIL/DataConf#dr-yoram-gdalyahu-vp-algorithms-mobileye---autonomous-driving-and-crowd-mapping
Guy Adini, Istra - Toy Models for Algorithmic Tradinghttps://github.com/DataHackIL/DataConf#id12
https://github.com/DataHackIL/DataConf#guy-adini-istra---toy-models-for-algorithmic-trading
DataConf 2017https://github.com/DataHackIL/DataConf#id13
https://github.com/DataHackIL/DataConf#dataconf-2017
DataConf 2017http://dataconf.org/
http://dataconf.org/http://dataconf.org/
https://www.meetup.com/DataHack/events/244004618/https://www.meetup.com/DataHack/events/244004618/
https://www.facebook.com/events/1623405514382356/https://www.facebook.com/events/1623405514382356/
Yakov Shambik, Mobileye - Eye of the Beholder: Object detection in Mobileye using Deep Neural Networks and other techniqueshttps://github.com/DataHackIL/DataConf#id14
https://github.com/DataHackIL/DataConf#yakov-shambik-mobileye---eye-of-the-beholder-object-detection-in-mobileye-using-deep-neural-networks-and-other-techniques
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Mobileye_Yakov_Shambik.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Mobileye_Yakov_Shambik.pdf
Ofer Ron, LivePerson - Concepts before machinery: Harnessing the power of domain expertise for machine-learning-based solutionshttps://github.com/DataHackIL/DataConf#id15
https://github.com/DataHackIL/DataConf#ofer-ron-liveperson---concepts-before-machinery-harnessing-the-power-of-domain-expertise-for-machine-learning-based-solutions
https://www.youtube.com/watch?v=wR2u7V8D5Y8&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=3https://www.youtube.com/watch?v=wR2u7V8D5Y8&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=3
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Alex Ran, Intuit - Using Data Science for Automated Accountinghttps://github.com/DataHackIL/DataConf#id16
https://github.com/DataHackIL/DataConf#alex-ran-intuit---using-data-science-for-automated-accounting
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Meir Maor, SparkBeyond - Developing Simple and Stable Machine Learning Modelshttps://github.com/DataHackIL/DataConf#id17
https://github.com/DataHackIL/DataConf#meir-maor-sparkbeyond---developing-simple-and-stable-machine-learning-models
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_SparkBeyond_Meir_Maor.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_SparkBeyond_Meir_Maor.pdf
Roii Spoliansky, PayPal - Active learning optimization as a function of label cost and mistake costhttps://github.com/DataHackIL/DataConf#id18
https://github.com/DataHackIL/DataConf#roii-spoliansky-paypal---active-learning-optimization-as-a-function-of-label-cost-and-mistake-cost
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_PayPal_Roii_Spoliansky.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_PayPal_Roii_Spoliansky.pdf
Gil Chamiel, Taboola - Don’t believe everything your network tells you: Uncertainty in deep learning for recommender systemshttps://github.com/DataHackIL/DataConf#id19
https://github.com/DataHackIL/DataConf#gil-chamiel-taboola---dont-believe-everything-your-network-tells-you-uncertainty-in-deep-learning-for-recommender-systems
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Adina Lederhendler, Neura - General vs. subpopulation-specific modeling: When and why you need to get specifichttps://github.com/DataHackIL/DataConf#id20
https://github.com/DataHackIL/DataConf#adina-lederhendler-neura---general-vs-subpopulation-specific-modeling-when-and-why-you-need-to-get-specific
https://www.youtube.com/watch?v=ft36Tq5FUz0&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=4https://www.youtube.com/watch?v=ft36Tq5FUz0&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=4
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Neura_Adina_Lederhandler.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Neura_Adina_Lederhandler.pdf
Yonatan Wexler, Orcam - Fast and Furious Face Recognition: Efficient metric learning for video stream datahttps://github.com/DataHackIL/DataConf#id21
https://github.com/DataHackIL/DataConf#yonatan-wexler-orcam---fast-and-furious-face-recognition-efficient-metric-learning-for-video-stream-data
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Orcam_Yonatan_Wexler.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Orcam_Yonatan_Wexler.pdf
Itamar Ben-Ari, Research Scientist @ Intel - Differentiable Memory Allocation Mechanism For Neural Computinghttps://github.com/DataHackIL/DataConf#id22
https://github.com/DataHackIL/DataConf#itamar-ben-ari-research-scientist--intel---differentiable-memory-allocation-mechanism-for-neural-computing
https://www.youtube.com/watch?v=DAHTNElXXgk&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=4https://www.youtube.com/watch?v=DAHTNElXXgk&list=PLZYkt7161wELbPfqY92vAEmKVhsyxg5Nk&index=4
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Intel_Itamar_Ben_Ari.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Intel_Itamar_Ben_Ari.pdf
Dr. Oshri, Rafael - Multi-agent deep reinforcement learning in communication networkshttps://github.com/DataHackIL/DataConf#id23
https://github.com/DataHackIL/DataConf#dr-oshri-rafael---multi-agent-deep-reinforcement-learning-in-communication-networks
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Rafael.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2017/DataConf_2017_Rafael.pdf
DataConf 2018https://github.com/DataHackIL/DataConf#id24
https://github.com/DataHackIL/DataConf#dataconf-2018
DataConf 2018http://dataconf.org/
https://www.youtube.com/playlist?list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53https://www.youtube.com/playlist?list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53
http://dataconf.org/http://dataconf.org/
https://www.meetup.com/DataHack/events/255082526/https://www.meetup.com/DataHack/events/255082526/
https://www.facebook.com/events/1967922793269453/https://www.facebook.com/events/1967922793269453/
Dana Kaner, Perimter X - Bootstrap, Random Forest and ll sorts of magichttps://github.com/DataHackIL/DataConf#id25
https://github.com/DataHackIL/DataConf#dana-kaner-perimter-x---bootstrap-random-forest-and-ll-sorts-of-magic
https://www.youtube.com/watch?v=ynkJVd6B13Uhttps://www.youtube.com/watch?v=ynkJVd6B13U
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_PerimeterX_Dana_Kaner.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_PerimeterX_Dana_Kaner.pdf
Pavel Levin, Booking.com - Where should I travel next? Modeling multi-destination trips with Recurrent Neural Networks.https://github.com/DataHackIL/DataConf#id26
https://github.com/DataHackIL/DataConf#pavel-levin-bookingcom---where-should-i-travel-next-modeling-multi-destination-trips-with-recurrent-neural-networks
https://www.youtube.com/watch?v=pwfwUA4ZShI&t=0s&index=5&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53https://www.youtube.com/watch?v=pwfwUA4ZShI&t=0s&index=5&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Booking_Pavel_Levin.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Booking_Pavel_Levin.pdf
Ari Bornstien, Microsoft - Beyond Word Embeddingshttps://github.com/DataHackIL/DataConf#id27
https://github.com/DataHackIL/DataConf#ari-bornstien-microsoft---beyond-word-embeddings
https://www.youtube.com/watch?v=zeYwMIDo05w&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=6https://www.youtube.com/watch?v=zeYwMIDo05w&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=6
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Microsoft_Ari_Bornstein.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Microsoft_Ari_Bornstein.pdf
Dr. Michal Shmueli-Scheuer, IBM Research - Conversational bots for customer supporthttps://github.com/DataHackIL/DataConf#id28
https://github.com/DataHackIL/DataConf#dr-michal-shmueli-scheuer-ibm-research---conversational-bots-for-customer-support
https://www.youtube.com/watch?v=i567nLfEGYs&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=9https://www.youtube.com/watch?v=i567nLfEGYs&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=9
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_IBM_Michal_Shmueli_Scheuer.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_IBM_Michal_Shmueli_Scheuer.pdf
Nofar Betzalel, Paypal - Semi-Supervised Learning for Tagging Coverage Extensionhttps://github.com/DataHackIL/DataConf#id29
https://github.com/DataHackIL/DataConf#nofar-betzalel-paypal---semi-supervised-learning-for-tagging-coverage-extension
https://www.youtube.com/watch?v=c4-3697xwys&index=7&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&t=0shttps://www.youtube.com/watch?v=c4-3697xwys&index=7&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&t=0s
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_PayPal_Nofar_Betzalel.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_PayPal_Nofar_Betzalel.pdf
Dr. Lev Faivishevsky, Intel Advanced Analytics - Using Deep-Learning to Detect Video distortionshttps://github.com/DataHackIL/DataConf#id30
https://github.com/DataHackIL/DataConf#dr-lev-faivishevsky-intel-advanced-analytics---using-deep-learning-to-detect-video-distortions
https://www.youtube.com/watch?v=FhMWZgs0kJ8&t=0s&index=8&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53https://www.youtube.com/watch?v=FhMWZgs0kJ8&t=0s&index=8&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Intel_Lev_Faivishevsky.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Intel_Lev_Faivishevsky.pdf
Prof. Danny Pfeffermann, Central Bureau for Statistics - Can Big Data Really Replace Traditional Surveys for theProduction of Official Statistics?https://github.com/DataHackIL/DataConf#id31
https://github.com/DataHackIL/DataConf#prof-danny-pfeffermann-central-bureau-for-statistics---can-big-data-really-replace-traditional-surveys-for-theproduction-of-official-statistics
https://www.youtube.com/watch?v=OcD20PkNj-w&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=10https://www.youtube.com/watch?v=OcD20PkNj-w&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=10
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Lamas_Danny_Pfeffermann.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Lamas_Danny_Pfeffermann.pdf
Avi Hendler-Bloom, MobilEye - Overcoming the Electronic Traffic Sign Problemhttps://github.com/DataHackIL/DataConf#id32
https://github.com/DataHackIL/DataConf#avi-hendler-bloom-mobileye---overcoming-the-electronic-traffic-sign-problem
https://www.youtube.com/watch?v=QN9gfUZUqDUhttps://www.youtube.com/watch?v=QN9gfUZUqDU
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Mobileye_Avi_Hendler_Bloom.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Mobileye_Avi_Hendler_Bloom.pdf
Daniel Benzaquen, Lightricks - AB testing at Scalehttps://github.com/DataHackIL/DataConf#id33
https://github.com/DataHackIL/DataConf#daniel-benzaquen-lightricks---ab-testing-at-scale
https://www.youtube.com/watch?v=-k1X2MRgGlYhttps://www.youtube.com/watch?v=-k1X2MRgGlY
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Lightricks_Daniel_Benzaquen.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Lightricks_Daniel_Benzaquen.pdf
Gil Chamiel, Taboola - Deep And Shallow Learning in Recommendation Systemshttps://github.com/DataHackIL/DataConf#id34
https://github.com/DataHackIL/DataConf#gil-chamiel-taboola---deep-and-shallow-learning-in-recommendation-systems
https://www.youtube.com/watch?v=nghXG5OiUno&index=12&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53https://www.youtube.com/watch?v=nghXG5OiUno&index=12&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Taboola_Gil_Chamiel.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Taboola_Gil_Chamiel.pdf
Oren Shamir, Innoviz Technologies - Neural networks for point clouds: adding the 3rd dimensionhttps://github.com/DataHackIL/DataConf#id35
https://github.com/DataHackIL/DataConf#oren-shamir-innoviz-technologies---neural-networks-for-point-clouds-adding-the-3rd-dimension
https://www.youtube.com/watch?v=aE3mfLm5dMA&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=11https://www.youtube.com/watch?v=aE3mfLm5dMA&t=0s&list=PLZYkt7161wEIjQOuWA93Tt4JS8DgCyz53&index=11
https://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Booking_Innoviz_Shamir.pdfhttps://github.com/DataHackIL/DataConf/blob/master/DataConf_2018/DataConf_2018_Booking_Innoviz_Shamir.pdf
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