Gently down the stream
A gentle introduction to Apache Kafka

Written and illustrated by Mitch Seymor

Learn about Kafka in a way i am sure you haven't seen before 😊

https://www.gentlydownthe.stream/

Photoshop detector AI called FALDetector aims to detect facial edits that warps the faces to make chin or jaw looks thinner, or the forehead smaller. However, it is not really accurate and consistent as it look at the individual pixels too much, as different resolution of the same person can result in different predictions with this AI model.

FALDetector:

Github:
https://github.com/peterwang512/FALdetector

Project page:
https://peterwang512.github.io/FALdetector/

Installation tutorial:
https://www.youtube.com/watch?v=fZSWAsjwQpE

Paper:
https://arxiv.org/pdf/1906.05856.pdf

Amazing Hackthon Solved Data Science/ML Project Collection

⭐️ 167

https://github.com/analyticsindiamagazine/MachineHack/tree/master/Hackathon_Solutions

The Only Probability Cheatsheet You'll Ever Need

https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf

source: https://github.com/wzchen/probability_cheatsheet

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Practical Deep Learning for Coders
Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD - the book and the course
🎬 8 lessons
⏰ 16 hours
https://course.fast.ai/

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Undergraduate Machine Learning (Nando de Freitas/University of British Columbia)

Author: prof Nando de Freitas
🎬 33 lessons
⏰ 21 hours

An undergraduate machine learning course. Lectures are filmed and put on YouTube with the slides posted on the course website. The course assignments are posted as well (no solutions, though). De Freitas is now a full-time professor at the University of Oxford and receives praise for his teaching abilities in various forums. Graduate version available.

https://www.cs.ubc.ca/~nando/340-2012/index.php

#machinelearning #datascience #statistics
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ACL Year-ROUND Mentorship

Incredible opportunity from NLP community of the Association for Computational Linguistics. The students all over the world can apply and get the mentorship in their research career during the whole year!
You can discuss anything — starting from the choice of the career to the questions how to manage your time and life.

More details here:
https://mentorship.aclweb.org/Home.html

CS231n: Convolutional Neural Networks for Visual Recognition
Stanford - Spring 2021

These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. You can also find google colab notebooks and all assignments here. For questions/concerns/bug reports, you can submit a pull request directly to their git repo.


🔗 https://cs231n.github.io/

#stanford #cnn #visual recognition
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Artificial Intelligence course by MIT

Professor: Patrick Winston, Ford Professor of Artificial Intelligence and Computer Science.
🎬 23 lessons
⏰ 17 hours

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.

🔗 Link to couse
🔗 Link to video lessons 🎬


#ai #artificialintellignece #mit
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AI Expert Roadmap

Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an AI expert.

What is actually pretty cool is that you can click in any part of roadmap and learn more about mentioned concept!

https://i.am.ai/roadmap/

#ai #artificialintellignece #ml #machinelearning #datascience #roadmap
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Introduction to Machine Learning Problem Framing
By Google

Estimated Course Length: 1 hour

https://developers.google.com/machine-learning/problem-framing

#machinelearning #ml
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ML_cheatsheets.pdf

30 Days of ML, free Kaggle challenge

Machine learning beginner → Kaggle competitor in 30 days.
Non-coders welcome.

Starts August 2nd!

FAQ

I already have some familiarity with Python and/or Machine Learning. Can I still join the program?
Anyone can join! You’ll get more out of the program if you’re not a very advanced Python user, or if you are relatively new to machine learning.

What is the time commitment for the program?
Assignments should take about 1 hour/day to complete.

How much is the program?
Nothing! All you need is a Kaggle account.

Do I need to bring my own GPU or deep learning workstation?
No, Kaggle provides free hosted notebooks with access to GPUs and TPUs to complete your data science projects.

🔗 https://www.kaggle.com/thirty-days-of-ml

Sign Up for the challenge.

#kaggle #python #machinelearning #ml
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Graph Neural Networks: Algorithms and Applications

A great presentation by Jian Tang about GNN basics, training many layers, self-supervised learning and statistical relational learning.