How to Reduce the Variance of Deep Learning Models in Keras Using Model Averaging Ensembles
#deeplearning #machinelearning
https://bit.ly/2PQlEVu
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#deeplearning #machinelearning
https://bit.ly/2PQlEVu
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#Statistics don't lie, but statisticians may
Data Science isn't tough, but Data Scientists should be.
#datascience #aspirants tell me the hurdles you are facing every day in your transition. I would like to hear out. I have a lot of friends in my network who can answer. Even I will.
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Data Science isn't tough, but Data Scientists should be.
#datascience #aspirants tell me the hurdles you are facing every day in your transition. I would like to hear out. I have a lot of friends in my network who can answer. Even I will.
❇️ @AI_Python_EN
🗣 @AI_Python_arXiv
✴️ @AI_Python
Amazing. Train a network to classify papers (accept/reject). Then run the network on the paper describing the network, and it classifies the paper as a strong reject. This is why we can't have nice paper classifiers.
https://arxiv.org/abs/1812.08775
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https://arxiv.org/abs/1812.08775
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🗣 @AI_Python_arXiv
✴️ @AI_Python
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
❇️ @AI_Python_EN
🗣 @AI_Python_arXiv
✴️ @AI_Python
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
❇️ @AI_Python_EN
🗣 @AI_Python_arXiv
✴️ @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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🗣 @AI_Python_arXiv
✴️ @AI_Python
http://bit.ly/2sSORWi
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🗣 @AI_Python_arXiv
✴️ @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
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🗣 @AI_Python_arXiv
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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
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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
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🗣 @AI_Python_arXiv
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Understanding LSTM Networks
By Christopher Olah: https://lnkd.in/eWJkwp3
#DeepLearning #LSTM #RecurrentNeuralNetworks
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🗣 @AI_Python_arXiv
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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
🔸 November 2018
🔸 November 2017
🔸 July 2018
🔸 April 2018
🔸 June 2018
🔸 September 2018
🔸 October 2018
🔸 August 2018
#DeepLearning #machinelearning #AI #Artificialinteligence #مقاله
❇️ @AI_Python_EN
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✴️ @AI_Python
🔸 November 2018
🔸 November 2017
🔸 July 2018
🔸 April 2018
🔸 June 2018
🔸 September 2018
🔸 October 2018
🔸 August 2018
#DeepLearning #machinelearning #AI #Artificialinteligence #مقاله
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✴️ @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 …. 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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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¬eId=BklCusRct7)
TLDR: Stop using linear interpolation!
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🗣 @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!
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
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 …. As a bonus, the name is Arabic & Spanish inspired! twitter JupyterProject Mona Jalal…
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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🗣 @AI_Python_arXiv
some of tqdm's bugs:
https://t.co/QflMyWcUTE
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🗣 @AI_Python_arXiv
Forwarded from AI, Python, Cognitive Neuroscience (🐻🦏🐋🦅🐕 Meysam Asgari)
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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 :)
Our channel in english: ✴️ @AI_Python_EN
Our Daily arXiv Channel: 🗣 @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
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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
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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv
AndrewYNg from LandingAI sharing his thoughts around #AI & #MachineLearning.
https://www.swarmapp.com/c/kLTdYT7cXAO
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🗣 @AI_Python_arXiv
https://www.swarmapp.com/c/kLTdYT7cXAO
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❇️ @AI_Python
🗣 @AI_Python_arXiv
"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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🗣 @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
✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv