Neurobiology and AI online meetings. Spike neurons, dendritic modelling and the cutting edge of Neuroscience and AI:
https://neuromatch.io/agenda/

A list of helpful docker containers for deploying Machine Learning projects: https://hub.docker.com/r/chansonz/ml_dev_env https://hub.docker.com/r/jupyter/datascience-notebook https://hub.docker.com/r/tensorflow/tensorflow https://hub.docker.com/r/pytorch/pytorch

Меня просили спалить годноту по терверу и матстату, итак тервер.

#statistics #матстат

Крутейший прикладной матстат, с кучей мат оптимизаций и подходов(в том числе и эвристических), разные лайфхаки и тонкости) Читать после освоения базы по терверу и матстату

#матстат #статистика #statistics

Stanford University has finally uploaded New Lectures on Machine Learning Course by Andrew Ng on YouTube:

https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU #stanforduniversity #AndrewNg

тут курсы уку бесплатные подвезли

не думала что доживу до такого

начался плотный онлайн курс от Neuromatch

https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw

http://www.neuromatchacademy.org/syllabus/

Machine learning deployment tools
https://www.mlflow.org/docs/latest/projects.html https://www.kubeflow.org

https://youtu.be/uDeQMpJ9yfY
Немного оффтоп, но очень важная тема для каждого человека, особенно для студентов. Тут очень грамотный и глубокий взгляд на универы от нашего коллеги из США. Автор делится мнением как надувался пузырь высшего образования

Need to use pandas, but not satisfied with it's performance? CuDF is a pandas-like library, but GPU-native. Is available to compute your dataframes operations on the GPUs :
GitHub - rapidsai/cudf: cuDF - GPU DataFrame Library
https://github.com/rapidsai/cudf

A nice collection of functional analysis books. It also contains my favorite one on this topic (guess which) 🥰🥰Enjoy https://github.com/Sky-Nik/fa/find/master

Large-scale Neural Solvers for Partial Differential Equations https://arxiv.org/abs/2009.03730

“DeepSpeed can train a language model with 1 trillion parameters using as few as 800 NVIDIA V100 GPUs”

Even “with a single V100, our users can run models of up to 13 billion parameters without running out of memory, while obtaining competitive throughput.”

https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/

Never read books about success, business and life couching because they ignore a factor of luck(and mostly are a waist of your precious time). Always stick to the real science in order to avoid misconceptions: https://youtu.be/3LopI4YeC4I

Wanna read some statistics or linear algebra books, but running out of money? No big deal, introducing my personal life hack - open source science books on many topics, a fully open sourced library: https://open.umn.edu/opentextbooks/


How to use it - just select preferred topic from the search line or select topic from the main page.

Hi, 1touch.io is looking for a data scientist to join our data analysis team!

Company: 1touch.io
Location: Kyiv, Ukraine (for now we’re attending the office once a week)
Salary: $1300-$2000 (can be discussed depending on experience)

We are looking for an ambitious highly motivated data scientist, who’s willing to participate in the development of a variety of AI modules, which are responsible for end-to-end processing of any kind of text/image data, which comes to the application.

Responsibilities:
- research of latest advances in the sphere of ML to continuously deliver new approaches and support existing pipelines;
- ability to take responsibility over any piece of standard ML cycle: - feature engineering, model development, model training, model evaluation and prediction, containers integration and deployment;
- integration testing, unit testing and benchmarking of the code;
- performing all of the data preprocessing/data engineering steps: - data fetching, data preprocessing, data labeling and cleaning;
- dockerization of ML components of the system;
- creation of basic documentation for the repo.

Requirement & skills:
- 2+ years of experience at the similar role;
- proficiency with Python 3.x;
- knowledge of tensorflow/pytorch, gensim, scikit-learn, keras, spacy, numpy, and similar ML libraries;
- basic understanding of how message brokers, CI pipelines, shell scripting work;
- understanding of statistics and probability theory;
- understanding and experience of implementation of ML approaches;
- strong understanding of business requirements;
- desire to participate in end-to-end delivery cycle;
experience with deep learning.

Nice to have:
- experience with Docker containers and services;
- advanced English knowledge.

Benefits:
- comfortable office in city center;
- friendly and highly professional atmosphere, laptop or workstation, corporate events;
- benefits package including competitive salary, medical insurance, bonuses and annual salary reviews;
- paid 14 sick leaves, 20 vacations and national holidays;
- great opportunities for professional growth and advancement;
- reimbursement for transportation expenses for out of town employers, parking place as an option;
- comfortable office facilities (kitchens, coffee/tea points, etc.).

Please write in Telegram to @vrcntr if you feel like you're interested!

Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress https://arxiv.org/abs/2009.13807