Looking for Masters and PhD level students for Paylocity's data science internship program! Students must be in their penultimate year of school, with strong knowledge of machine learning and software engineering. You'll work with Paylocity's incredible talented Product Owners to translate our customers' business needs into data science needs and deliver features that enable next generation HR analytics.

https://2000recruiting.paylocity.com/recruiting/jobs/Details/2767/Paylocity/Data-Scientist-Intern---Summer-2020

❇️ @AI_Python_EN

Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity
Mandlekar et al.:
https://lnkd.in/fG9PGJK
Blog:
https://lnkd.in/fUjqAgH
Webpage:
https://lnkd.in/f5Vx6um
#Robotics #HumanComputerInteraction #MachineLearning

❇️ @AI_Python_EN

Decoding the Black Box: An Important Introduction to Interpretable Machine Learning Models in…

#machinelearning

https://bit.ly/2N5QFb3

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Mish is now even supported on YOLO v3 backend. Couldn't have been more elated with how rewarding this project has been. Link to repository -

https://github.com/digantamisra98/Mish

#neuralnetworks #mathematics #algorithms #deeplearning #machinelearning

❇️ @AI_Python_EN

Experience-Embedded Visual Foresight
Yen-Chen et al.:
https://arxiv.org/abs/1911.05071

Demo:
http://yenchenlin.me/evf/

#DeepLearning #MachineLearning #Robotics

❇️ @AI_Python_EN

Grid search vs randomized search?
💡 What are the pros and cons of grid search? Pros: • Grid search is great when you need to fine-tune hyperparameters over a small search space automatically. • For example, if you have 100 different datasets that you expect to be similar (e.g. solving the same problem repeatedly with different populations), you can use grid search to automatically fine-tune the hyperparameters for each model. Cons: • Grid search is computationally expensive and inefficient, often searching over parameter space that has very little chance of being useful, resulting it being extremely slow. It's especially slow if you need to search a large space since it's complexity increases exponentially as more hyperparameters are optimized.
💡 What are the pros and cons of randomized search? Pros: • Randomized search does a good job finding near-optimal hyperparameters over a very large search space relatively quickly and doesn't suffer from the same exponential scaling problem as grid search. Cons: • Randomized search does not fine-tune the results as much as grid search does since it typically does not test every possible combination of parameters.
#datascience
👉 Free training -> http://bit.ly/dsdj-webinar


❇️ @AI_Python_EN

Machine Learning w.r.t meditation routine.
Machine before meditation = underfitting
Machine after meditation = optimal fitting
Planning of meditation = overfitting
#datascience

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Trying the online demo of GPT-2 based text generator.
https://transformer.huggingface.co/doc/gpt2-large

#deeperlearning #machinelearning

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Four keys to #machinelearning on the edge
Machine learning is hard. Moving your ML model to embedded devices can be even harder.


https://bit.ly/2KjUEh6

❇️ @AI_Python_EN

AI IN 2018: A YEAR IN REVIEW

#artificialintelligence #AI #MachineLearning

https://medium.com/@AINowInstitute/ai-in-2018-a-year-in-review-8b161ead2b4e

❇️ @AI_Python_EN

25 Best Free #Dataset s for Machine Learning

https://www.edureka.co/blog/25-best-free-datasets-machine-learning/

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MIT Technology Review:
A #NeuralNet solves the three-body problem 100 million times faster

#MachineLearning #mathematics

🔰 NeuralNet

🔰 Paper

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Brown University’s Data Science Initiative (DSI) seeks applicants for a lecturer, senior lecturer, or distinguished senior lecturer. The initial appointment is for a 3-year period (renewable with potential for promotion and longer-term contracts). The position involves teaching four courses

per year and providing administrative or advising support for student programs. We seek candidates who will contribute to our overall intellectual culture. Lecturers with substantial research participation and supporting funds may be eligible for periodic course release. Please see our Interfolio posting for more details and to apply. 
https://apply.interfolio.com/70943

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3 Ways to Encode Categorical Variables for Deep Learning

https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/

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Deep Learning in mobile and wireless networking:
https://arxiv.org/pdf/1803.04311.pdf
ML in telecom:
https://jisajournal.springeropen.com/articles/10.1186/s13174-018-0087-2

❇️ @AI_Python_EN

How to process images in Python

https://morioh.com/p/f469f74855f6

#Python

❇️ @AI_Python_EN

Evolving Neural Networks
A tutorial on evolutionary algorithms

https://towardsdatascience.com/evolving-neural-networks-b24517bb3701

#NeuralNetworks #ReinforcementLearning

❇️ @AI_Python_EN

Release: CCNet is our new tool for extracting high-quality and large-scale monolingual corpora from CommonCraw in more than a hundred languages.
Paper:
https://arxiv.org/abs/1911.00359
Tool:
https://github.com/facebookresearch/cc_net

❇️ @AI_Python_EN

Who is winning the #AI race : China 🇨🇳, Europe🇪🇺 or US 🇺🇸?

Interesting 106 page report from Aug 2019 by "Center for data innovation"
I personally believe that innovation will come from a true borderless exchange of technology & talent in democratic and responsible societies.

#artificialintelligence #machinelearning

❇️ @AI_Python_EN

Adobe Research is offering audio research internships in San Francisco and Seattle in 2020. We are looking for PhD students who are excited about pushing the state of the art in the field and having a 

Please use “Audio Research Internship” as the title of your application email.

...more

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