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

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How to process images in Python

https://morioh.com/p/f469f74855f6

#Python

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Evolving Neural Networks
A tutorial on evolutionary algorithms

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

#NeuralNetworks #ReinforcementLearning

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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

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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

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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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The Interactive Media Group at Microsoft Research, Redmond has several openings for research internships. For over 20 years, our interns have been conducting influential research published in top-tier venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, JASA. We are looking for motivated students in various topics including, but not limited to,

computer vision, machine learning, object detection and tracking,scene understanding,3D reconstruction, computational photography, learning from simulation, mixed/augmented reality ,unsupervised representation learning , 
multimodal learning for video understanding, physically-based sound synthesis and propagation, deep learning for vision, graphics, and acoustics

For details on our research group and projects, please see:

http://research.microsoft.com/en-us/groups/graphics/

We are located at Microsoft headquarters in Redmond, near Seattle, Washington. We strive to make our internships fun, productive, and profitable (salary, and transportation, etc.).

HOW TO APPLY:

If interested, please apply here:

https://careers.microsoft.com/us/en/job/736933/Research-Intern-Computer-Vision-Machine-Learning-Graphics-Sound-Propagation-within-IMG

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We are looking for summer interns at Nokia Bell Labs to work on machine learning research projects in summer 2020. Job details are as follows: 

Human Augmented Sensing Summer Student

MSc or PhD students in CS, computational and applied math, OR, EE, or related fields (eg, computational physics and chemistry).

Have a deep understanding of machine learning/IP networking and expertise in related areas such as machine learning tools and applications, software defined networking and algorithmic aspects of networking.

Proven track record of research contributions and publications in leading and internationally recognized conferences and journals.

Student Criteria:

Overall GPA of 3.0/4.0 or above.

Must be enrolled as a full-time student.

Work Authorization:  

U.S. Citizen or Permanent Resident preferred.  Will accept students on F-1 Visa dependent upon program requirements or may consider sponsoring short-term J1 Visa PhD students dependent upon program requirements.

To apply, please go to

https://aluperf.referrals.selectminds.com/jobs/bell-labs-intern-augmented-human-sensing-36720

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Conversation with Gilbert Strang, a professor of mathematics at MIT & an inspiring teacher of linear algebra to millions of students around the world through MIT OpenCourseWare.

https://www.youtube.com/watch?v=lEZPfmGCEk0

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Another nice visual guide by Jay Alammar about how you can use BERT to do text classification. In particular, he’s using DistilBERT to create sentence embeddings which is then used as an input for logistic regression. Code is also provided! Check it out! #deeplearning #machinelearning #NLP
📝 Article:
https://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/

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Machine Learning From Scratch

https://github.com/eriklindernoren/ML-From-Scratch
#ArtificialIntelligence #DeepLearning #MachineLearning

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You can now easily deploy your TensorFlow models in a Google Colab or Jupyter notebook with TensorFlow Extended (TFX)! Very nice! Check out the article and the tutorial for more details.
#deeplearning #machinelearning

📝 Article:
https://blog.tensorflow.org/2019/11/introducing-tfx-interactive-notebook.html?m=1

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Countdown to NeurIPS 2019 continues... (5th of 8 studies my team will present) A lot of companies ranging from small startups to large corporate giants are releasing Explainable AI toolkits and core features using popular XAI methods like LIME, SHAP, Integrated Gradients, etc. However, one begs the question: Do the explanations provided by these XAI methods really reflect the decisions made by machine learning algorithm? In this study, we introduce a measurable way in an attempt to answer this question, and study some of the most popular XAI methods to see where they stand for deep neural networks. The results may surprise you...
#deeplearning #neurips
https://arxiv.org/abs/1910.07387

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4 Traits, qualities that a data scientist must seek ...

1) Technical bar: Data science teams work everyday in SQL, specifically in Postgres, and expect candidates to know Python/some fluency in some sort of statistical language. Also, someone who is really comfortable with querying really large datasets.

2) Communication: we’re in roles where a lot of our day-to-day is spent getting great insights or building models and communicating results of that to stakeholders, whether that’s product managers, marketing folks or finance. It’s super key that data science candidates have good communication skills.

3) Grit, tenacity and willingness to solve hard problems: Things that DS teams solve are generally hard problems. My hope is that anyone who joins the data science team is excited about hard problems and bumping against hard challenges.

4) Passion for the arts and passion for the mission: This is not the most important but great to have.
#datascience

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Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.

#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork

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