Names for collections of code in various languages:
A pile of JavaScript
A crystal of Haskell
An undefinedness of C++
A liability of Python
A French grad student of OCaml
An ambition of Rust
A bank of COBOL
A postmodernism of Perl
An accident of C
A Unabomber of Forth
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A pile of JavaScript
A crystal of Haskell
An undefinedness of C++
A liability of Python
A French grad student of OCaml
An ambition of Rust
A bank of COBOL
A postmodernism of Perl
An accident of C
A Unabomber of Forth
βοΈ @AI_Python_EN
π£ @AI_Python_arXiv
β΄οΈ @AI_Python
9,216 IBM Power9 CPUs and 27,648 Nvidia Volta GPUs #Supercomputer performs 200 quadrillion calculations per second, #USA tops #China for the world's fastest #computer #AI #DataScience #DataAnalytics #IoT #BigData
http://bit.ly/2sSORWi
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http://bit.ly/2sSORWi
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β΄οΈ @AI_Python
The FEYNMAN technique of learning:
STEP 1 - Pick and study a topic
STEP 2 - Explain the topic to someone, like a child, who is unfamiliar with the topic
STEP 3 - Identify any gaps in your understanding
STEP 4 - Review and Simplify!
- Richard Feynman
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STEP 1 - Pick and study a topic
STEP 2 - Explain the topic to someone, like a child, who is unfamiliar with the topic
STEP 3 - Identify any gaps in your understanding
STEP 4 - Review and Simplify!
- Richard Feynman
βοΈ @AI_Python_EN
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β΄οΈ @AI_Python
An Amoeba-Based Computer Calculated Approximate Solutions to a Very Hard Math Problem
Article by Daniel Oberhaus: https://lnkd.in/eHJRTBS
#biocomputers
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Article by Daniel Oberhaus: https://lnkd.in/eHJRTBS
#biocomputers
βοΈ @AI_Python_EN
π£ @AI_Python_arXiv
β΄οΈ @AI_Python
The Unreasonable Effectiveness of Recurrent Neural Networks
Blog (2015) by Andrej Karpathy: https://lnkd.in/eNC7BK5
#DeepLearning #NeuralNetworks #RecurrentNeuralNetworks #RNN
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Blog (2015) by Andrej Karpathy: https://lnkd.in/eNC7BK5
#DeepLearning #NeuralNetworks #RecurrentNeuralNetworks #RNN
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Can Neural Networks Remember?
Slides by Vishal Gupta: https://lnkd.in/e_EUYGv
#RecurrentNeuralNetworks #LongShortTermMemory #LSTM #neuralnetworks
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Slides by Vishal Gupta: https://lnkd.in/e_EUYGv
#RecurrentNeuralNetworks #LongShortTermMemory #LSTM #neuralnetworks
βοΈ @AI_Python_EN
π£ @AI_Python_arXiv
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Understanding LSTM Networks
By Christopher Olah: https://lnkd.in/eWJkwp3
#DeepLearning #LSTM #RecurrentNeuralNetworks
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By Christopher Olah: https://lnkd.in/eWJkwp3
#DeepLearning #LSTM #RecurrentNeuralNetworks
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Best of arXiv.org for AI, Machine Learning, and Deep Learning
πΈ November 2018
πΈ November 2017
πΈ July 2018
πΈ April 2018
πΈ June 2018
πΈ September 2018
πΈ October 2018
πΈ August 2018
#DeepLearning #machinelearning #AI #Artificialinteligence #Ω ΩΨ§ΩΩ
βοΈ @AI_Python_EN
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πΈ November 2018
πΈ November 2017
πΈ July 2018
πΈ April 2018
πΈ June 2018
πΈ September 2018
πΈ October 2018
πΈ August 2018
#DeepLearning #machinelearning #AI #Artificialinteligence #Ω ΩΨ§ΩΩ
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β΄οΈ @AI_Python
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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 β¦. As a bonus, the name is Arabic & Spanish inspired! twitter JupyterProject
Mona Jalal Siad: tqdm stems from ΨͺΩΨ―Ω which means "progress"
#python
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Mona Jalal Siad: tqdm stems from ΨͺΩΨ―Ω 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¬eId=BklCusRct7)
TLDR: Stop using linear interpolation!
β΄οΈ @AI_Python_EN
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π£ @AI_Python_arXiv
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¬eId=BklCusRct7)
TLDR: Stop using linear interpolation!
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
When ML has no common sense π
#ML #MachineLearning
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#ML #MachineLearning
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AI, Python, Cognitive Neuroscience
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 β¦. As a bonus, the name is Arabic & Spanish inspired! twitter JupyterProject Mona Jalalβ¦
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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some of tqdm's bugs:
https://t.co/QflMyWcUTE
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βοΈ @AI_Python
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Forwarded from AI, Python, Cognitive Neuroscience (π»π¦ππ¦
π Meysam Asgari)
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π If you like our channel, i invite you to share it with your friends:
Our channel in english: β΄οΈ @AI_Python_EN
Our Daily arXiv Channel: π£ @AI_Python_Arxiv
BTW: Thank you for joining :)
Our channel in english: β΄οΈ @AI_Python_EN
Our Daily arXiv Channel: π£ @AI_Python_Arxiv
BTW: Thank you for joining :)
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Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Article
Code
Online Demo
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Article
Code
Online Demo
π£ @AI_Python_arXiv
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βοΈ @AI_Python
ProjectJupyterβ© notebook server running on home_assistantβ© Hassio on an β¦#Raspberry_Piβ© viewed in the iOS app on my Appleβ© 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
The screenshot is from a Gender-focused #Kaggle Kernel I did sometime back : https://lnkd.in/fXCDHjv
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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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βοΈ @AI_Python
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AndrewYNg from LandingAI sharing his thoughts around #AI & #MachineLearning.
https://www.swarmapp.com/c/kLTdYT7cXAO
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https://www.swarmapp.com/c/kLTdYT7cXAO
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βοΈ @AI_Python
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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
β΄οΈ @AI_Python_EN
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π£ @AI_Python_arXiv
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
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
Deep Latent-Variable Models for Natural Language
Tutorial by Kim et al.: https://lnkd.in/eUHDAnP
#NLP #pytorch #unsupervisedlearning
β΄οΈ @AI_Python_EN
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Tutorial by Kim et al.: https://lnkd.in/eUHDAnP
#NLP #pytorch #unsupervisedlearning
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
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, thereβs no telling how much youβre going to improve.
Sooner or later you're going to build so much practical skill that no other learning resource will teach you. π
#datascience #machinelearning
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
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, thereβs no telling how much youβre going to improve.
Sooner or later you're going to build so much practical skill that no other learning resource will teach you. π
#datascience #machinelearning
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv