Stop Installing Tensorflow Using pip for Performance Sake!
π Link Review
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π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
π Link Review
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π£ @AI_Python_arXiv
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Forwarded from arXiv
π A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation
π£ Meysam Asgari-Chenaghlu, Mohammad-Ali Balafar, Mohammad-Reza Feizi-Derakhshi
https://www.sciencedirect.com/science/article/pii/S0165168418303700
π£ @AI_Python_arXiv
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βοΈ @AI_Python
π£ Meysam Asgari-Chenaghlu, Mohammad-Ali Balafar, Mohammad-Reza Feizi-Derakhshi
https://www.sciencedirect.com/science/article/pii/S0165168418303700
π£ @AI_Python_arXiv
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β
Problem Solving with Algorithms and Data Structures using Python
#Book
π Link Review
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π£ @AI_Python_arXiv
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#Book
π Link Review
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π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
Using GANs to generate Master[Finger]Prints that unlock 22-78% phones sensors (dep. on security level of sensor)
https://arxiv.org/pdf/1705.07386.pdf
doesn't get much more "adversarial" than that.
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
https://arxiv.org/pdf/1705.07386.pdf
doesn't get much more "adversarial" than that.
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
Ph.D. position in deep learning for signal processing at University of Oldenburg
The Computational Audition Group at the Department of Medical Physics and
Acoustics, University of Oldenburg, Germany, seeks a highly motivated
PhD student
(75 % of full time, TV-L E13, 3 years, with possibility of extension)
in the field of Deep Learning for Signal Processing
Our group investigates audio signal recognition with multi-channel microphone
arrays, identifying important sound sources in real-world acoustic scenes, and
performing source separation to enhance e.g. an attended speech signal.
Applicants for the position must hold an academic university degree (master or
equivalent) in physics, engineering, computer science or a related discipline
and should have knowledge in at least one of the following fields:
- machine learning,
- speech recognition,
- acoustic event detection,
- speech and audio processing.
Excellent knowledge of scientific programming languages such as Python or
MATLAB, as well as excellent English language skills are required.
The position is funded by the newly established DFG collaborative research
centre CRC 1330 "Hearing Acoustics", which aims at improving human communication
in real-world acoustic scenes and is a long-term collaboration of researchers
from Oldenburg, Aachen and Munich. For more information about the CRC see
https://www.uni-oldenburg.de/sfb1330.
The Computational Audition Group is part of the Department of Medical Physics
and Acoustics at University of Oldenburg. We offer an international scientific
environment and world-class research facilities. For more information about the
group and the department see https://uol.de/computational-audition/ and
https://uol.de.de/en/mediphysics-acoustics/.
The University of Oldenburg is dedicated to increase the percentage of women in
science. Therefore, female candidates are strongly encouraged to apply. In
accordance to Β§ 21 Section 3 NHG, female candidates with equal qualifications
will be preferentially considered. Applicants with disabilities will be given
preference in case of equal qualification.
Applications including a CV, a motivation letter, copies of relevant
certificates and the name and contact details of two references should be sent
as single pdf-file to joern.anemueller@uni-oldenburg.de or to
Dr. JΓΆrn AnemΓΌller
Faculty VI
Department of Medical Physics and Acoustics
UniversitΓ€t Oldenburg
D - 26111 Oldenburg
Tel. (+49) 441 798 3610
The application deadline is 28th of November 2018.
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
The Computational Audition Group at the Department of Medical Physics and
Acoustics, University of Oldenburg, Germany, seeks a highly motivated
PhD student
(75 % of full time, TV-L E13, 3 years, with possibility of extension)
in the field of Deep Learning for Signal Processing
Our group investigates audio signal recognition with multi-channel microphone
arrays, identifying important sound sources in real-world acoustic scenes, and
performing source separation to enhance e.g. an attended speech signal.
Applicants for the position must hold an academic university degree (master or
equivalent) in physics, engineering, computer science or a related discipline
and should have knowledge in at least one of the following fields:
- machine learning,
- speech recognition,
- acoustic event detection,
- speech and audio processing.
Excellent knowledge of scientific programming languages such as Python or
MATLAB, as well as excellent English language skills are required.
The position is funded by the newly established DFG collaborative research
centre CRC 1330 "Hearing Acoustics", which aims at improving human communication
in real-world acoustic scenes and is a long-term collaboration of researchers
from Oldenburg, Aachen and Munich. For more information about the CRC see
https://www.uni-oldenburg.de/sfb1330.
The Computational Audition Group is part of the Department of Medical Physics
and Acoustics at University of Oldenburg. We offer an international scientific
environment and world-class research facilities. For more information about the
group and the department see https://uol.de/computational-audition/ and
https://uol.de.de/en/mediphysics-acoustics/.
The University of Oldenburg is dedicated to increase the percentage of women in
science. Therefore, female candidates are strongly encouraged to apply. In
accordance to Β§ 21 Section 3 NHG, female candidates with equal qualifications
will be preferentially considered. Applicants with disabilities will be given
preference in case of equal qualification.
Applications including a CV, a motivation letter, copies of relevant
certificates and the name and contact details of two references should be sent
as single pdf-file to joern.anemueller@uni-oldenburg.de or to
Dr. JΓΆrn AnemΓΌller
Faculty VI
Department of Medical Physics and Acoustics
UniversitΓ€t Oldenburg
D - 26111 Oldenburg
Tel. (+49) 441 798 3610
The application deadline is 28th of November 2018.
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Recommended by DeepMind
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search" - off-policy training of agents by reasoning about alternative outcomes under counterfactual actions. https://arxiv.org/abs/1811.06272
#Paper
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search" - off-policy training of agents by reasoning about alternative outcomes under counterfactual actions. https://arxiv.org/abs/1811.06272
#Paper
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
hot subject
Stop Sitting On All That Data & Do Something With It! βοΈ
π Link Review
#BigData #Analytics #AI #MachineLearning #DeepLearning
π£ @AI_Python_arXiv
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βοΈ @AI_Python
Stop Sitting On All That Data & Do Something With It! βοΈ
π Link Review
#BigData #Analytics #AI #MachineLearning #DeepLearning
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Forwarded from arXiv
πUnsupervised learning with contrastive latent variable models.
πPDF
#arXiv
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
πPDF
#arXiv
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Forwarded from arXiv
Forwarded from Code Community βοΈ (π Amir Armanπ)
πΈComputer Science Flash Cards
#computer_science #Flash_Card
π Link
γ°γ°γ°γ°γ°
Β© @Code_Community
#computer_science #Flash_Card
π Link
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Β© @Code_Community
Whatβs New in Deep Learning Research: How Google Builds Curiosity Into Reinforcement Learning Agents
π Link Review
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π Link Review
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Neural Network Programming with TensorFlow
#Ϊ©ΨͺΨ§Ψ¨
π Download ππ
β https://t.me/ai_python_en/62 β
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
#Ϊ©ΨͺΨ§Ψ¨
π Download ππ
β https://t.me/ai_python_en/62 β
βοΈ @AI_Python
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
Packt.Neural.Network.Programming.with.TensorFlow.1788390393.epub
6.1 MB
Neural Network Programming with TensorFlow
#Ϊ©ΨͺΨ§Ψ¨
βοΈ @AI_Python
β΄οΈ @AI_Python_EN
Β© @Code_Community
#Ϊ©ΨͺΨ§Ψ¨
βοΈ @AI_Python
β΄οΈ @AI_Python_EN
Β© @Code_Community
π
β΄οΈ @AI_Python_EN
β΄οΈ @AI_Python_EN
Forwarded from arXiv
Overview Page: Unsupervised Neural Networks that Fight in a Minimax Game, to Learn the Statistics of Data, or to Achieve Artificial Curiosity in Reinforcement Learning (since the early 1990s):
https://lnkd.in/gxC8Awg
π£ @AI_Python_arXiv
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βοΈ @AI_Python
https://lnkd.in/gxC8Awg
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
βͺοΈData Science Projects:
β The Data Science IPython Notebooks:
=> This repository is filled with IPython notebooks that cover different topics, going from Kaggle competitions to big data and deep learning.
[ https://lnkd.in/dW3WBi6 ]
β The Pattern Classification:
=> Tutorials and examples to solve and understand machine learning and pattern classification tasks.
[ https://lnkd.in/d9PGxHm ]
β Deep Learning In Python:
=> This repository is the way to go!
[ https://lnkd.in/d-hNVCD ]
More ...
βͺοΈData Science News
βͺοΈData Science Books
βͺοΈData Science Talks
βͺοΈR for Data Science Talks
βͺοΈPython for Data Science Talks
βͺοΈBig Data Talks
βͺοΈData Science Podcasts
βͺοΈData Science Webinars
βͺοΈData Science Tutorials
βͺοΈData Science Community
βͺοΈData Science Courses
Refer to the full article
[ https://lnkd.in/dmKmx_D ]
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
β The Data Science IPython Notebooks:
=> This repository is filled with IPython notebooks that cover different topics, going from Kaggle competitions to big data and deep learning.
[ https://lnkd.in/dW3WBi6 ]
β The Pattern Classification:
=> Tutorials and examples to solve and understand machine learning and pattern classification tasks.
[ https://lnkd.in/d9PGxHm ]
β Deep Learning In Python:
=> This repository is the way to go!
[ https://lnkd.in/d-hNVCD ]
More ...
βͺοΈData Science News
βͺοΈData Science Books
βͺοΈData Science Talks
βͺοΈR for Data Science Talks
βͺοΈPython for Data Science Talks
βͺοΈBig Data Talks
βͺοΈData Science Podcasts
βͺοΈData Science Webinars
βͺοΈData Science Tutorials
βͺοΈData Science Community
βͺοΈData Science Courses
Refer to the full article
[ https://lnkd.in/dmKmx_D ]
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Introduction to Artificial Intelligence
Lectures for INFO8006 - Introduction to Artificial Intelligence, Fall 2018.
By Gilles Louppe: https://lnkd.in/eixRYiJ
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Lectures for INFO8006 - Introduction to Artificial Intelligence, Fall 2018.
By Gilles Louppe: https://lnkd.in/eixRYiJ
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
AI Pioneer Yoshua Bengio Says Universities Deserve More Credit
By Sam Shead: https://lnkd.in/ehFYW5i
#artificialintelligence #deeplearning #machinelearning
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
By Sam Shead: https://lnkd.in/ehFYW5i
#artificialintelligence #deeplearning #machinelearning
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
Efficiently measuring a quantum device using machine learning
π paper : https://arxiv.org/abs/1810.10042
π£ @AI_Python_arXiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π paper : https://arxiv.org/abs/1810.10042
π£ @AI_Python_arXiv
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βοΈ @AI_Python
Andrew Ng : Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
π Paper
π GitHub
π£ @AI_Python_arXiv
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π Paper
π GitHub
π£ @AI_Python_arXiv
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βοΈ @AI_Python