❇️ کانال "یادگیری ماشین در علم"
♦️آموزش، اخبار، همکاری در پروژه مشترک و آگهی استخدام در زمینه ⬅️ هوش مصنوعی، یادگیری ماشین، علوم عصبی و کلان داده ♦️

☯️ Python - R - Java - Matlab ☯️

xبه ما بپیوندید👇
@ml_in_science

------------

گروه لینکداین مرتبط با کانال "یادگیری ماشین در علم"
❇️ "Ai & BigData" LinkedIn group

join us👇
https://www.linkedin.com/groups/8721739/

💠 Amazon opens its internal machine learning courses to all for free:
🌎 https://aws.amazon.com/training/learning-paths/machine-learning/

شنبه ها با یادگیری کوانتومی ماشین 👇
#quant_comp_inf_ml
#quantum
#quant_ml_comp_algo

https://youtu.be/M0R12fcTUvs
منظور از محاسبه کوانتومی چیست؟، توسط Seth Lloyd از دانشگاه MIT

https://youtu.be/SSIww8LBQCs
"الگوریتمهای کوانتومی" توسط Peter Hoyer از دانشگاه کلگری، کانادا

https://youtu.be/KTVtMKo3g80
الگوریتمهای کوانتومی برای "دسته بندی" و یادگیری ماشین. ارائه توسط Lordanis Kerenidis از دانشگاه Paris Diderot، فرانسه

سلام...اگر اهل شنیدن #پادکست هستین و علاوه بر یادگیری ماشین از #عکاسی هم خوشتون میاد یا از دوربین موبایل استفاده میکنین این یه گفتگوی خیلی خیلی عمومی درباره تاثیر هوش مصنوعی بر عکاسی هست.
#podcast_ml
https://overcast.fm/+IlguKviKY

#Machinelearning for beginner s
#page count=128
#Year=2017
#book
@Machine_learn

#job_opportunity_ml
#statistical_physics
#machine_learning
#computational_biology

Dear colleague,

We are looking for a post-doc candidate at the crossroad of statistical
physics, machine learning, and computational biology, interested in the
theoretical and applied aspect of data-driven modeling.

Thanks to recent progress in machine learning, machine learning can be used to establish models of complex systems, which remain out of reach with standard first-principle methods. The goal of the post doctoral project will be two-fold:

(1) develop unsupervised machine learning tools and apply statistical
physics methods and concepts to better understand how these methods operate and learn from data. Different unsupervised architectures will be studied and compared, including Boltzmann Machines, Restricted Boltzmann Machines, and (Variational) Autoencoders.

(2) apply these methods to model proteins from sequence data, with special emphasis on the prediction of mutational effects and mutational paths in the trypsin enzyme, in connection with high-throughput experiments by C. Nizak and O. Rivoire at College de France.

The post-doc will be located in the Department of Physics at the Ecole
Normale Superieure in Paris, under the supervision of S. Cocco and R.
Monasson. The duration of the position is of two years. Post-doc
candidates are expected to have solid knowledge in statistical physics,
inference methods and data analysis, and both analytical and computer
programming skills. Moreover he/she should have a deep interest and
possibly a previous experience in computational biology and/or
bioinformatics.

Applications should be sent by email to [email protected] or
[email protected] by January 15, 2018.
Please help us distribute this announcement.

best regards,
Simona Cocco and Rémi Monasson