LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model
https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f
https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f
Medium
Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model
In this post I show you how to predict stock prices using a forecasting LSTM model
Dear All,
The University of Geneva Particle Physics and Data Science Departments invite applications for
Doctoral Assistants and postdocs
to work on an interdisciplinary project to provide HEP and solar astronomy with robust deep density machine-learning (ML) tools with focus on predictive, generative and anomaly detection models, with the ultimate objective to maximise the LHC’s sensitivity to discover physics beyond the Standard Model, as well as to optimise solar flare prediction.
The project provides funding for a total of 12 positions composed of PhD students and postdocs for 4 years.
The successful candidates have the opportunity to be part of and shape this interdisciplinary project spanning all the way from the development of theoretical ML foundations to their practical applications and generalisation in real-world science questions.
For application details see:
https://inspirehep.net/jobs/1806673 (for postdoc applications)
https://inspirehep.net/jobs/1806674 (for PhD applications)
Applications should be received by August 17 2020 and the position is expected to start by January 2021 (possibly earlier if requested). For further information please contact [email protected].
Best,
Tobias
The University of Geneva Particle Physics and Data Science Departments invite applications for
Doctoral Assistants and postdocs
to work on an interdisciplinary project to provide HEP and solar astronomy with robust deep density machine-learning (ML) tools with focus on predictive, generative and anomaly detection models, with the ultimate objective to maximise the LHC’s sensitivity to discover physics beyond the Standard Model, as well as to optimise solar flare prediction.
The project provides funding for a total of 12 positions composed of PhD students and postdocs for 4 years.
The successful candidates have the opportunity to be part of and shape this interdisciplinary project spanning all the way from the development of theoretical ML foundations to their practical applications and generalisation in real-world science questions.
For application details see:
https://inspirehep.net/jobs/1806673 (for postdoc applications)
https://inspirehep.net/jobs/1806674 (for PhD applications)
Applications should be received by August 17 2020 and the position is expected to start by January 2021 (possibly earlier if requested). For further information please contact [email protected].
Best,
Tobias
inspirehep.net
Machine Learning in ATLAS - INSPIRE
The University of Geneva Particle Physics and Computer Science Departments invite applications forPostdocs and doctoral assistantsto work on an interdis...
❇️ Text Summary For Wikipedia Articles With Simple Natural Language Processing Concept In Python
https://www.taheramlaki.com/blog/articles/text-summary-nlp/
https://www.taheramlaki.com/blog/articles/text-summary-nlp/
A list of educational resources curated by DeepMind Scientists and
Engineers for students interested in learning more about artifical intelligence,
machine learning and other related topics.
https://storage.googleapis.com/deepmind-media/research/New_AtHomeWithAI%20resources.pdf
Engineers for students interested in learning more about artifical intelligence,
machine learning and other related topics.
https://storage.googleapis.com/deepmind-media/research/New_AtHomeWithAI%20resources.pdf
💠 Remote NLP Engineer
Company: Memora Health
Place: Anywhere
Salary: $100,000 - $180,000 / yr
❇️ https://angel.co/company/memora-health/jobs/943666-nlp-engineer
Company: Memora Health
Place: Anywhere
Salary: $100,000 - $180,000 / yr
❇️ https://angel.co/company/memora-health/jobs/943666-nlp-engineer
❇️ Inverted CERN School of Computing 2020 - ONLINE EVENT - Sept. 28 to Oct. 2
Dear All,
The 13th edition of the Inverted CERN School of Computing (iCSC 2020), will take place as an online event from September 28 to October 2, 2020 (in the afternoons).
An excellent programme is planned, consisting of lectures and hands-on exercises selected from a range of proposals by past CSC students, and focusing on the following domains:
• Programming Paradigms and Design Patterns
• Heterogeneous Programming with OpenCL
• Computational Fluid Dynamics
• Reconstruction and Imaging
• Modern C++ features
• Big Data processing with SQL
Attendance is free and open to anyone. Connection details (link to the videoconferencing room) will be sent by e-mail to registered participants - therefore if you are interested, please register. You are not obliged to attend the full event - indeed you can simply attend the classes that interest you the most!
Certificate of attendance will be provided to those who attend at least 80% of the lectures, and take a short evaluation test after the school end.
More details, including the timetable: https://indico.cern.ch/e/iCSC-2020.
Please feel free to forward this message to any of your colleagues who might be interested. Thank you!
Kind regards,
The CSC Team
CERN School of Computing
https://csc.web.cern.ch/
Dear All,
The 13th edition of the Inverted CERN School of Computing (iCSC 2020), will take place as an online event from September 28 to October 2, 2020 (in the afternoons).
An excellent programme is planned, consisting of lectures and hands-on exercises selected from a range of proposals by past CSC students, and focusing on the following domains:
• Programming Paradigms and Design Patterns
• Heterogeneous Programming with OpenCL
• Computational Fluid Dynamics
• Reconstruction and Imaging
• Modern C++ features
• Big Data processing with SQL
Attendance is free and open to anyone. Connection details (link to the videoconferencing room) will be sent by e-mail to registered participants - therefore if you are interested, please register. You are not obliged to attend the full event - indeed you can simply attend the classes that interest you the most!
Certificate of attendance will be provided to those who attend at least 80% of the lectures, and take a short evaluation test after the school end.
More details, including the timetable: https://indico.cern.ch/e/iCSC-2020.
Please feel free to forward this message to any of your colleagues who might be interested. Thank you!
Kind regards,
The CSC Team
CERN School of Computing
https://csc.web.cern.ch/
Indico
Inverted CERN School of Computing 2020
The 13th Inverted CERN School of Computing (iCSC 2020) consists of classes (lectures, exercises, demonstration and consultations) given by former CERN School of Computing students. The Inverted School provides a platform to share their knowledge by turning…
💠 CALL FOR PAPERS
❇️ Machine Learning and the Physical Sciences Workshop at the 34th
Conference on Neural Information Processing Systems (NeurIPS) December
11, 2020 NeurIPS 2020 is a Virtual-only Conference
https://ml4physicalsciences.github.io/
ABOUT
Machine learning methods have had great success in learning complex
representations of data that enable novel modeling and data processing
approaches in many scientific disciplines. Physical sciences span
problems and challenges at all scales in the universe: from finding
exoplanets in trillions of sky pixels, to developing solutions to the
quantum many-body problem and combinatorial problems, to detecting
anomalies in event streams from the Large Hadron Collider, to predicting
how extreme weather events will vary with climate change. Tackling a
number of associated data-intensive tasks including, but not limited to,
segmentation, computer vision, sequence modeling, causal reasoning,
generative modeling, and probabilistic inference are critical for
furthering scientific discovery in these and many other areas. In
addition to using machine learning models for scientific discovery, the
ability to interpret what a model has learned is receiving an increasing
amount of attention.
In this targeted workshop, we aim to bring together computer scientists,
mathematicians and physical scientists who are interested in applying
machine learning to various outstanding physical problems including in
inverse problems, approximating physical processes, understanding what a
learned model represents, and connecting tools and insights from the
physical sciences to the study of machine learning models. In
particular, the workshop invites researchers to contribute short papers
(extended abstracts) that demonstrate cutting-edge progress in the
application of machine learning techniques to real-world problems in the
physical sciences and/or using physical insights to understand and
improve machine learning techniques.
By bringing together machine learning researchers and physical
scientists who apply machine learning, we expect to strengthen the
interdisciplinary dialogue, introduce exciting new open problems to the
broader community, and stimulate the production of new approaches to
solving challenging open problems in the sciences. Invited talks from
leading individuals in both communities will cover the state-of-the-art
techniques and set the stage for this workshop.
SCOPE
We invite researchers to submit papers in the following and related
areas:
* Application of machine learning to physical sciences
* Generative models
* Likelihood-free inference
* Variational inference
* Simulation-based inference
* Implicit models
* Probabilistic models
* Model interpretability
* Approximate Bayesian computation
* Strategies for incorporating prior scientific knowledge into machine learning algorithms
* Experimental design
* Any other area related to the subject of the workshop
Submissions of completed projects as well as high-quality works in
progress are welcome. All accepted short papers (extended abstracts)
will be made available on the workshop website. This does not constitute
an archival publication or formal proceedings; authors retain full
copyright of their work and are free to publish their extended work in
another journal or conference. We allow submission of extended abstracts
that overlap with papers that are under review or have been recently
published in a conference or a journal. However, we do not accept cross
submissions of the same extended abstract to multiple workshops at
NeurIPS. Submissions will be kept confidential until they are accepted
and authors confirm that they can be included in the workshop. If a
submission is not accepted, or withdrawn for any reason, it will be kept
confidential and not made public.
Accepted work will be presented as posters during the workshop. Each
accepted work entering the poster sessions would have an accompanying
pre-recorded 5-minute video. Please note that at least one coauthor of
each accepted paper will be expected to have a NeurIPS c
❇️ Machine Learning and the Physical Sciences Workshop at the 34th
Conference on Neural Information Processing Systems (NeurIPS) December
11, 2020 NeurIPS 2020 is a Virtual-only Conference
https://ml4physicalsciences.github.io/
ABOUT
Machine learning methods have had great success in learning complex
representations of data that enable novel modeling and data processing
approaches in many scientific disciplines. Physical sciences span
problems and challenges at all scales in the universe: from finding
exoplanets in trillions of sky pixels, to developing solutions to the
quantum many-body problem and combinatorial problems, to detecting
anomalies in event streams from the Large Hadron Collider, to predicting
how extreme weather events will vary with climate change. Tackling a
number of associated data-intensive tasks including, but not limited to,
segmentation, computer vision, sequence modeling, causal reasoning,
generative modeling, and probabilistic inference are critical for
furthering scientific discovery in these and many other areas. In
addition to using machine learning models for scientific discovery, the
ability to interpret what a model has learned is receiving an increasing
amount of attention.
In this targeted workshop, we aim to bring together computer scientists,
mathematicians and physical scientists who are interested in applying
machine learning to various outstanding physical problems including in
inverse problems, approximating physical processes, understanding what a
learned model represents, and connecting tools and insights from the
physical sciences to the study of machine learning models. In
particular, the workshop invites researchers to contribute short papers
(extended abstracts) that demonstrate cutting-edge progress in the
application of machine learning techniques to real-world problems in the
physical sciences and/or using physical insights to understand and
improve machine learning techniques.
By bringing together machine learning researchers and physical
scientists who apply machine learning, we expect to strengthen the
interdisciplinary dialogue, introduce exciting new open problems to the
broader community, and stimulate the production of new approaches to
solving challenging open problems in the sciences. Invited talks from
leading individuals in both communities will cover the state-of-the-art
techniques and set the stage for this workshop.
SCOPE
We invite researchers to submit papers in the following and related
areas:
* Application of machine learning to physical sciences
* Generative models
* Likelihood-free inference
* Variational inference
* Simulation-based inference
* Implicit models
* Probabilistic models
* Model interpretability
* Approximate Bayesian computation
* Strategies for incorporating prior scientific knowledge into machine learning algorithms
* Experimental design
* Any other area related to the subject of the workshop
Submissions of completed projects as well as high-quality works in
progress are welcome. All accepted short papers (extended abstracts)
will be made available on the workshop website. This does not constitute
an archival publication or formal proceedings; authors retain full
copyright of their work and are free to publish their extended work in
another journal or conference. We allow submission of extended abstracts
that overlap with papers that are under review or have been recently
published in a conference or a journal. However, we do not accept cross
submissions of the same extended abstract to multiple workshops at
NeurIPS. Submissions will be kept confidential until they are accepted
and authors confirm that they can be included in the workshop. If a
submission is not accepted, or withdrawn for any reason, it will be kept
confidential and not made public.
Accepted work will be presented as posters during the workshop. Each
accepted work entering the poster sessions would have an accompanying
pre-recorded 5-minute video. Please note that at least one coauthor of
each accepted paper will be expected to have a NeurIPS c
onference
registration that includes the workshop session and participate in one
of the virtual poster sessions.
Examples of accepted abstracts from previous years can be found here:
https://ml4physicalsciences.github.io/
SUBMISSION INSTRUCTIONS
Submissions should be anonymized short papers (extended abstracts) up to
4 pages in PDF format, typeset using the NeurIPS style (
https://neurips.cc/Conferences/2020/PaperInformation/StyleFiles ). The
authors are required to include a short statement (one paragraph) about
the potential broader impact of their work, including any ethical
aspects and future societal consequences, which may be positive or
negative. The broader impact statement should come after the main paper
content (see the NeurIPS style files for an example). The impact
statement and references do not count towards the page limit. Appendices
are discouraged, and reviewers are not expected to read beyond the first
4 pages and the impact statement. A workshop-specific modified NeurIPS
style file will be provided for the camera-ready versions, after the
author notification date.
Submission page: https://cmt3.research.microsoft.com/ML4PS2020
IMPORTANT DATES
* Submission deadline: October 2, 2020 (extended from September 25, 2020), 23:59 PDT
* Author notification: October 23, 2020
* Camera-ready (final) paper deadline: November 23, 2020
* Workshop: December 11, 2020
CONFIRMED SPEAKERS
Estelle Inack (Perimeter Institute) Phiala Shanahan (MIT) Laura Waller
(UC Berkeley) (More to be confirmed)
ORGANIZERS
Atilim Gunes Baydin (University of Oxford) Juan Felipe Carrasquilla
(Vector Institute / University of Waterloo) Adji Bousso Dieng (Columbia
University) Karthik Kashinath (NERSC, Lawrence Berkeley National Lab)
Gilles Louppe (University of Liège) Brian Nord (Fermilab) Michela
Paganini (Facebook AI Research) Savannah Thais (Princeton University)
STEERING COMMITTEE
Anima Anandkumar (California Institute of Technology / NVIDIA) Kyle
Cranmer (New York University) Shirley Ho (Flatiron Institute / Princeton
University / Carnegie Mellon University) Prabhat (NERSC, Lawrence
Berkeley National Lab) Lenka Zdeborová (Institut de Physique
Théorique, CEA Saclay)
REGISTRATION
Participants should refer to the NeurIPS 2020 website (
https://neurips.cc/ ) for information on how to register for the
workshop.
CONTACT
Please direct all questions and comments to Atilim Gunes Baydin <
[email protected] >. Please include “[ML4PS NeurIPS 2020]” in
the subject line.
-------------------------------------------------------------
Visit this CMS message (to reply or unsubscribe) at:
https://hypernews.cern.ch/HyperNews/CMS/get/machine-learning/184.html
registration that includes the workshop session and participate in one
of the virtual poster sessions.
Examples of accepted abstracts from previous years can be found here:
https://ml4physicalsciences.github.io/
SUBMISSION INSTRUCTIONS
Submissions should be anonymized short papers (extended abstracts) up to
4 pages in PDF format, typeset using the NeurIPS style (
https://neurips.cc/Conferences/2020/PaperInformation/StyleFiles ). The
authors are required to include a short statement (one paragraph) about
the potential broader impact of their work, including any ethical
aspects and future societal consequences, which may be positive or
negative. The broader impact statement should come after the main paper
content (see the NeurIPS style files for an example). The impact
statement and references do not count towards the page limit. Appendices
are discouraged, and reviewers are not expected to read beyond the first
4 pages and the impact statement. A workshop-specific modified NeurIPS
style file will be provided for the camera-ready versions, after the
author notification date.
Submission page: https://cmt3.research.microsoft.com/ML4PS2020
IMPORTANT DATES
* Submission deadline: October 2, 2020 (extended from September 25, 2020), 23:59 PDT
* Author notification: October 23, 2020
* Camera-ready (final) paper deadline: November 23, 2020
* Workshop: December 11, 2020
CONFIRMED SPEAKERS
Estelle Inack (Perimeter Institute) Phiala Shanahan (MIT) Laura Waller
(UC Berkeley) (More to be confirmed)
ORGANIZERS
Atilim Gunes Baydin (University of Oxford) Juan Felipe Carrasquilla
(Vector Institute / University of Waterloo) Adji Bousso Dieng (Columbia
University) Karthik Kashinath (NERSC, Lawrence Berkeley National Lab)
Gilles Louppe (University of Liège) Brian Nord (Fermilab) Michela
Paganini (Facebook AI Research) Savannah Thais (Princeton University)
STEERING COMMITTEE
Anima Anandkumar (California Institute of Technology / NVIDIA) Kyle
Cranmer (New York University) Shirley Ho (Flatiron Institute / Princeton
University / Carnegie Mellon University) Prabhat (NERSC, Lawrence
Berkeley National Lab) Lenka Zdeborová (Institut de Physique
Théorique, CEA Saclay)
REGISTRATION
Participants should refer to the NeurIPS 2020 website (
https://neurips.cc/ ) for information on how to register for the
workshop.
CONTACT
Please direct all questions and comments to Atilim Gunes Baydin <
[email protected] >. Please include “[ML4PS NeurIPS 2020]” in
the subject line.
-------------------------------------------------------------
Visit this CMS message (to reply or unsubscribe) at:
https://hypernews.cern.ch/HyperNews/CMS/get/machine-learning/184.html
❇️PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
💠https://arxiv.org/abs/2004.00452v1
---------------------
https://github.com/facebookresearch/pifuhd
💠https://arxiv.org/abs/2004.00452v1
---------------------
https://github.com/facebookresearch/pifuhd
GitHub
GitHub - facebookresearch/pifuhd: High-Resolution 3D Human Digitization from A Single Image.
High-Resolution 3D Human Digitization from A Single Image. - facebookresearch/pifuhd
☯️ آگهی استخدام
⬅️ شرح موقعیت شغلی
شرکت دانش بنیان پویا فناوران کوثر (pfkvision.com) با چهارده سال سابقه فعال در حیطه هوش مصنوعی و پردازش تصویر و متن برای تکمیل تیم تحلیل داده خود از کارشناسان نرم افزار با تجربه دارای شرایط زیر برای همکاری دعوت به عمل می آورد:
✅ برنامه نویس مسلط به Python
✅ آشنایی با NoSQL , Linux , Distributed System , Microservice
✅ آشنایی با ابزارهایی مثل: Kafka, Hadoop, Spark, Docker
فرد متقاضی باید شرایط عمومی زیر را داشته باشد:
💠 دارای کارت پایان خدمت
💠 حداقل یکسال سابقه کار
💠 برنامه میان مدت برای کار در ایران
💠 روحیه کار جمعی ، پیگیری ، سرچ و یادگیری ، روحیه انجام پروژه و داکیومنت سازی
🔴 لطفا افراد متقاضی رزومه خود را به آدرس ایمیل [email protected] بفرستند.
⬅️ شرح موقعیت شغلی
شرکت دانش بنیان پویا فناوران کوثر (pfkvision.com) با چهارده سال سابقه فعال در حیطه هوش مصنوعی و پردازش تصویر و متن برای تکمیل تیم تحلیل داده خود از کارشناسان نرم افزار با تجربه دارای شرایط زیر برای همکاری دعوت به عمل می آورد:
✅ برنامه نویس مسلط به Python
✅ آشنایی با NoSQL , Linux , Distributed System , Microservice
✅ آشنایی با ابزارهایی مثل: Kafka, Hadoop, Spark, Docker
فرد متقاضی باید شرایط عمومی زیر را داشته باشد:
💠 دارای کارت پایان خدمت
💠 حداقل یکسال سابقه کار
💠 برنامه میان مدت برای کار در ایران
💠 روحیه کار جمعی ، پیگیری ، سرچ و یادگیری ، روحیه انجام پروژه و داکیومنت سازی
🔴 لطفا افراد متقاضی رزومه خود را به آدرس ایمیل [email protected] بفرستند.