β
Free Book Online
Artificial Intelligence in Business Gets Real
π Link Review
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Artificial Intelligence in Business Gets Real
By S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira#book
π Link Review
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Twisted: Event-driven networking engine written in Python
https://www.twistedmatrix.com
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https://www.twistedmatrix.com
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Top 15 Python Libraries for Data Science
link
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link
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Medium
Top 15 Python Libraries for Data Science in 2017
As Python has gained a lot of traction in the recent years in Data Science industry, I wanted to outline some of its most useful librariesβ¦
Finding Similar Quora Questions with Word2Vec and Xgboost
link
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link
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Recurrent Neural Networks by Example in Python:
Using a Recurrent Neural Network to Write Patent Abstracts
link
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Using a Recurrent Neural Network to Write Patent Abstracts
link
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Predicting Professional Playersβ Chess Moves with Deep Learning
link
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link
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β Online Book
Reinforcement Learning: An Introduction
π Link Review
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Reinforcement Learning: An Introduction
π Link Review
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NVIDIA CEO Jensen Huang addresses 700+ attendees of SC18, where he revealed the rapid adoption of the NVIDIA T4 cloud GPU.
π Link Review
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Improving Neural Networks Hyperparameter Tuning, Regularization, and More
β Link Review
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β Link Review
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Andrew Ng launches βAI for Everyone,β a new Coursera program aimed at business professionals.
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The latest The machine learning Daily!
#paper
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#paper
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Stop Installing Tensorflow Using pip for Performance Sake!
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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
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π£ Meysam Asgari-Chenaghlu, Mohammad-Ali Balafar, Mohammad-Reza Feizi-Derakhshi
https://www.sciencedirect.com/science/article/pii/S0165168418303700
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β
Problem Solving with Algorithms and Data Structures using Python
#Book
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#Book
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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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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
Forwarded from arXiv
πUnsupervised learning with contrastive latent variable models.
πPDF
#arXiv
π£ @AI_Python_arXiv
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βοΈ @AI_Python
πPDF
#arXiv
π£ @AI_Python_arXiv
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βοΈ @AI_Python
Forwarded from arXiv