A visual introduction to machine learning, Part II

https://bit.ly/2N0T42K

#AI #DeepLearning #MachineLearning #DataScience

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Data science = Statistics +
Data preprocessing +
Machine learning +
Scientific inquiry +
Visualization +
Business Analytics +
Programming +
Empathy +
Communication + ...

—> To solve a real problem.

Data Science involves anything you do with data to solve real problems.

Be a problem solver.

And use data to help guide you to the solution.

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François Chollet:

Pre-trained network for image super resolution (in Keras): https://github.com/idealo/image-super-resolution … An evening project would be to export it to TF.js to run in the browser on user-uploaded photos

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Learn probabilistic programming with TensorFlow Probability, from the ground up. The Bayesian Methods for Hackers book is now available in open source in TFP! Read post here ↓

Link Review


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30 Free Courses in #NeuralNetworks, #MachineLearning, #Algorithms, and #AI — https://bit.ly/2p7zQMB #abdsc #BigData #DataScience #DeepLearning #DataScientists

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The Athlete and the Machine: New Trends in #AI and Sports Technology https://buff.ly/2MJzIiG #MachineLearning

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Exciting news from #NeurIPS – the European Laboratory for Learning and Intelligent Systems (ELLIS) has been announced! The centre will support research and help industry leverage #AI.

https://nvda.ws/2roKRfK

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The Nytimes Data Science Group is searching for multiple full-time data scientists with a focus on machine learning. This is a great group of people working on interesting and important problems.

More info: https://nytimes.wd5.myworkdayjobs.com/en-US/DataInsights/job/New-York-NY/Data-Scientist--machine-learning-_REQ-004142

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😁
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Excited to present practical fairness paper "Why is My Classifier Discriminatory?" at #NeurIPS2018

papers.nips.cc/paper/7613-why-is-my-classifier-discriminatory

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NeurIPS 2018 video talk collection #NeurIPS2018

https://buff.ly/2EaUFBC

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👉 If you like our channel, i invite you to share it with your friends:
Our channel in english: ✴️ @AI_Python_EN
Our Daily arXiv Channel: 🗣 @AI_Python_Arxiv

BTW: Thank you for joining :)

Very comprehensive article on transfer learning. It covers the theory behind transfer learning in general and then how it can be used for deep learning. He then also presented two hands-on case studies where he used transfer learning in a CV classification task for first a binary class problem and then second multi-label classes. Code is also provided using Keras with the TensorFlow backend. Definitely check this article out. Transfer learning has a high practical importance for machine learning practitioners.
#deeplearning #machinelearning

Article: https://lnkd.in/daa6_UB
Github: https://lnkd.in/dhxXcRg

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Papers with Code: https://paperswithcode.com

#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #Technology


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PracticalAI: A practical approach to learning machine learning

By Goku Mohandas: https://lnkd.in/eyFbdCC

#machinelearning #naturallanguageprocessing #jupyter #python #pytorch


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Comixify: Transform video into a comics. They used a 2-stage approach: (a) frame selection and (b) style transfer. The results look pretty cool!

paper: https://lnkd.in/eszcexU
demo: https://lnkd.in/edrtfPd
test video: https://lnkd.in/ebWpPRD

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The AI art gallery from NeurIPS Creativity workshop

AI Art Gallery: https://aiartonline.com

#NeurIPS2018

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My 13-minute oral presentation at hashtag#NeurIPS2018 summarizing our world models paper. I felt like the weight of the world (model) was finally lifted off my shoulders after giving the talk.

article → https://lnkd.in/fa36JNH
paper → https://lnkd.in/gPjH_NJ


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A Programmer’s Introduction to Mathematics. It teaches someone with programming knowledge and experience how to engage with mathematics. Achieve this goal largely because of the implicit overlap in the content and ways of thinking between math and programming.
Until now. If you’re a programmer who wants to really grok math, this book is for you.
GitHub: Link
#Book #کتاب

Download :
https://t.iss.one/ai_python_en/190

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Dr. Jeremy Kun:

A Programmer’s Introduction to Mathematics.

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Computer Vision problems are often solved using a Machine Learning approach. In Machine Learning, we learn from data.

What if you do not have enough data? Well, there is still some hope.

If you are able to segment your shape out, you can use Hu Moments for shape matching. These moments are invariant to translation, scale, and rotation and can identify the shape even if it has undergone those transformations.

You will learn about raw moments, central moments and Hu moments in our post today.

We also show how to use moments for matching shapes.

As always, we are sharing code in C++ and Python.

https://lnkd.in/eVk_J5M

#AI #MachineLearning #ComputerVision #OpenCV


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