Wanna see progress of a long running operation easily in your Jupyter notebook? Use the wonderful tqdm module - https://github.com/tqdm/tqdm#ipython-jupyter-integration …. As a bonus, the name is Arabic & Spanish inspired! twitter JupyterProject
Mona Jalal Siad: tqdm stems from تقدم which means "progress"
#python

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Eirikur Agustsson Research Scientist Google

this paper on how to properly interpolate samples from GANs and VAEs has been accepted to ICLR 2019!

Paper: Optimal Transport Maps For Distribution Preserving Operations on Latent Spaces of Generative Models (
https://openreview.net/forum?id=BklCusRct7&noteId=BklCusRct7)
TLDR: Stop using linear interpolation!

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When ML has no common sense 😂
#ML #MachineLearning

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Could you also consider taking a look at "fastprogress", our recent replacement for tqdm, which has some nice extra features (see the readme) and avoids
some of tqdm's bugs:

https://t.co/QflMyWcUTE


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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 :)

Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Article
Code
Online Demo
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ProjectJupyter⁩ notebook server running on home_assistant⁩ Hassio on an ⁦#Raspberry_Pi⁩ viewed in the iOS app on my Apple⁩ iPhone, what a time to be alive

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Based on 2018 HackerRank's Developer survey, #Javascript #Java #Python stand out as the top 3 expected Programming languages but what's next is more important. That's being Language Agnostic!

This is very important especially in #DataScience and #MachineLearning where we always put R vs Python, but with market expecting Language Agnostic Developers, It's good to have both the languages at your disposal.

The screenshot is from a Gender-focused #Kaggle Kernel I did sometime back : https://lnkd.in/fXCDHjv

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AndrewYNg from LandingAI sharing his thoughts around #AI & #MachineLearning.

https://www.swarmapp.com/c/kLTdYT7cXAO

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"Godel Machines, Meta-Learning, and LSTMs" - interview with Juergen Schmidhuber

Juergen Schmidhuber is the co-creator of long short-term memory networks (LSTMs) which are used in billions of devices today for speech recognition, translation, and much more. Over 30 years, he has proposed a lot of interesting, out-of-the-box ideas in artificial intelligence including a formal theory of creativity. This conversation is part of the Artificial Intelligence podcast and the MIT course 6.S099: Artificial General Intelligence. The conversation and lectures are free and open to everyone

#MachineLearning #AI

https://youtu.be/3FIo6evmweo

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High hopes for 2019

#MachineLearning

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Deep Latent-Variable Models for Natural Language

Tutorial by Kim et al.: https://lnkd.in/eUHDAnP

#NLP #pytorch #unsupervisedlearning


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Right now, there's a better investment than learning data science.

Doing data science.

I don't mean to ignore learning data science as a whole. There are some awesome MOOCs, articles, books, videos, and resources out there, but what I really want to put emphasis on is for you to start doubling down on creating more data science projects.

A great data science project:
- Where you treat your skill set like an investment portfolio.
- Where you learn a concept and immediately apply what you just learned.
- Where you take the time to document, work out examples, and build a toy project.
- Where you start doing more of these hands-on style of learning and it keeps you thinking about how to improve.

And you don't have to start out with the latest and greatest data science project.

You can just start with an idea & let your curiosity guide you through the way. As you get stuck, learn how to solve it and keep progressing. Because all great work begins with something small.

Do a project -> Put it on GitHub -> Share your work -> Get feedback -> Improve & Repeat

If you do this, there’s no telling how much you’re going to improve.

Sooner or later you're going to build so much practical skill that no other learning resource will teach you. 🙂

#datascience #machinelearning

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Why Does Batch Normalization Work?

Blog by Abay Bektursun: https://lnkd.in/eR3cVjm

#BatchNormalization #DeepLearning #MachineLearning

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The major advancements in Deep Learning in 2018

Blog by Javier Couto: https://lnkd.in/erT7Uq9

#deeplearning #machinelearning #transferlearning

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GAN — LSGAN (How to be a good helper?) – Jonathan Hui – Medium

#deeplearning #machinelearning #transferlearning

Link Review

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Top Resoures Deep learning Reading List

#deeplearning #Data

🌎 Link Review
🌎 Most Cited Deep Learning Papers
🌎 Understanding CNNs Part 3
🌎 Understanding CNNs Part 2
🌎 Understanding CNNs Part 1
🌎 23 Deep Learning Papers To Get You Started
🌎 Deep learning Reading List

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Influence Attacks on Machine Learning

https://youtu.be/zgAzCVk3qgQ
Mark Sherman

explains how #deeplearning is playing an increasing role in developing new applications and how adversaries can attack machine learning systems in a variety of ways.

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He's making a training set, he's checking it twice, running inference to figure out who's been naughty or nice, Santa Claus is cooommmming to town. Professor Reza Zadeh

#machinelearning

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Interested in understanding neural networks for #NLProc ? Looking for reading material for your winter break? Check out our new paper, "Analysis Methods in Neural Language Processing: A Survey", to appear in TACL.
Preprint: https://arxiv.org/abs/1812.08951
Website: https://boknilev.github.io/nlp-analysis-methods


#machinelearning

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#Bayesian Optimization framework can intelligently trade off experiments w/ varying cost & fidelity. We achieve strong regret bounds as well as state-of-the-art performance on multiple real-world #datasets! Preprint: https://arxiv.org/abs/1811.00755v1


#machinelearning

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