Yoshua: Research is like a random exploration guided by intuition. It's okay to fail, but more important is to try. At an informal event at MILA Montreal
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β΄οΈ @AI_Python_EN
March 30, 2019
Are you a Data Scientists? Do you use Jupyter? Please help us understand how do you consume content and get connected with other professionals Just answer this 3 minute survey : http://bit.ly/Jupyter-survey-1 #DataScience #MachineLearning
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β΄οΈ @AI_Python_EN
Typeform
Jupyter survey
Turn data collection into an experience with Typeform. Create beautiful online forms, surveys, quizzes, and so much more. Try it for FREE.
March 30, 2019
Successful deployed an image classifier using #flask and #docker #100DaysOfMLCode #100daysofcode #MachineLearning #DeepLearning
https://github.com/hrishikeshmane87933/keras_webapp
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https://github.com/hrishikeshmane87933/keras_webapp
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GitHub
hrishikeshmane87933/keras_webapp
Contribute to hrishikeshmane87933/keras_webapp development by creating an account on GitHub.
March 30, 2019
Bill Gates: A.I. is like nuclear energy β 'both promising and dangerous' - CNBC Read more here: https://ift.tt/2uuayNC #ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT
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β΄οΈ @AI_Python_EN
March 30, 2019
MIT Introduction to #DeepLearning http://bit.ly/2JOvTf4 #MachineLearning #TensorFlow
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March 31, 2019
Checklist for debugging neural networks
http://bit.ly/2HSI0W5 #AI #DeepLearning #MachineLearning #DataScience
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http://bit.ly/2HSI0W5 #AI #DeepLearning #MachineLearning #DataScience
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March 31, 2019
How to write a good machine learning tutorial.
https://bit.ly/2TFUTF6
#MachineLearning #DeepLearning
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https://bit.ly/2TFUTF6
#MachineLearning #DeepLearning
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March 31, 2019
March 31, 2019
ArcFace: Additive Angular Margin Loss for Deep Face Recognition. The author used PyTorch 1.0 which is nice.
"We present arguably the most extensive experimental
evaluation of all the recent state-of-the-art face recognition
methods on over 10 face recognition benchmarks including
a new large-scale image database with trillion level
of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. We release all refined training data, training codes, pre-trained models and training logs , which will help reproducet he results in this paper."
https://lnkd.in/e5Q2qP3
https://lnkd.in/ezWbVhH
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"We present arguably the most extensive experimental
evaluation of all the recent state-of-the-art face recognition
methods on over 10 face recognition benchmarks including
a new large-scale image database with trillion level
of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. We release all refined training data, training codes, pre-trained models and training logs , which will help reproducet he results in this paper."
https://lnkd.in/e5Q2qP3
https://lnkd.in/ezWbVhH
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March 31, 2019
This week's #machinelearning Q&A is on Underfitting vs Overfitting -
π‘ How can you tell if your model is underfitting your data?
If your training and validation error are both relatively equal and very high, then your model is most likely underfitting your training data.
π‘ How can you tell if your model is overfitting your data?
If your training error is low and your validation error is high, then your model is most likely overfitting your training data.
π Do you have any favorite heuristics that you use to detect under and over fitting in your models?
#datascience
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π‘ How can you tell if your model is underfitting your data?
If your training and validation error are both relatively equal and very high, then your model is most likely underfitting your training data.
π‘ How can you tell if your model is overfitting your data?
If your training error is low and your validation error is high, then your model is most likely overfitting your training data.
π Do you have any favorite heuristics that you use to detect under and over fitting in your models?
#datascience
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March 31, 2019
Emoticons were born on this day in 1881 on the pages of Puck Magazine under the heading "Typographical Art," depicting four emotions: joy, melancholy, indifference, and astonishment https://www.brainpickings.org/2012/12/21/100-diagrams-that-changed-the-world/
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β΄οΈ @AI_Python_EN
April 1, 2019
Play with face editing SC-FEGAN directly in GoogleColab Notebook:
https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/SC_FEGAN.ipynb
Others: https://github.com/zaidalyafeai/Notebooks/blob/master/README.md
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https://colab.research.google.com/github/zaidalyafeai/Notebooks/blob/master/SC_FEGAN.ipynb
Others: https://github.com/zaidalyafeai/Notebooks/blob/master/README.md
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April 1, 2019
Wishes for TensorFlow/Keras π€
- A full merge between Keras and TF
- Make the transition from Keras to custom layers seamless
- Less announcements and more clarity on the existing API family
- An official experimental toolbox (similar to the fastai library)
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- A full merge between Keras and TF
- Make the transition from Keras to custom layers seamless
- Less announcements and more clarity on the existing API family
- An official experimental toolbox (similar to the fastai library)
β΄οΈ @AI_Python_EN
April 1, 2019
Want to know why training on small data is the future? And more importantly, why Andrew named his daughter Nova? Learn why in Andrewβs chat with MIT Tech Review's Will Knight at #EmTechDigital 2019: http://bit.ly/2VdaGwO
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β΄οΈ @AI_Python_EN
April 1, 2019
Protecting your #DeepLearning model will be the key area to focus on from cyber attacks to your models and algorithms.
Placing these is public cloud environments may severely affect your ability to protect these models and algorithms.
You need to prepare to defend these.
What are these adversarial attacks?
1. l2-norm attacks: in these attacks the attacker aims to minimize squared error between the adversarial and original image. These typically result in a very small amount of noise added to the image.
2. lβ-norm attacks: this is perhaps the simplest class of attacks which aim to limit or minimize the amount that any pixel is perturbed in order to achieve an adversaryβs goal.
3. l0-norm attacks: these attacks minimize the number of modified pixels in the image.
Below is an example of an l2-norm attack where the left is classified as jeep but the right as a minivan.
#cyberattacks #algorithms #models #deeplearning
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Placing these is public cloud environments may severely affect your ability to protect these models and algorithms.
You need to prepare to defend these.
What are these adversarial attacks?
1. l2-norm attacks: in these attacks the attacker aims to minimize squared error between the adversarial and original image. These typically result in a very small amount of noise added to the image.
2. lβ-norm attacks: this is perhaps the simplest class of attacks which aim to limit or minimize the amount that any pixel is perturbed in order to achieve an adversaryβs goal.
3. l0-norm attacks: these attacks minimize the number of modified pixels in the image.
Below is an example of an l2-norm attack where the left is classified as jeep but the right as a minivan.
#cyberattacks #algorithms #models #deeplearning
β΄οΈ @AI_Python_EN
April 1, 2019
You want to be a data scientist ...?
First read this excellent tutorial by https://lnkd.in/eKrDyhN: "How sure are we? Two approaches to statistical inference"
https://lnkd.in/e5JBrN4
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First read this excellent tutorial by https://lnkd.in/eKrDyhN: "How sure are we? Two approaches to statistical inference"
https://lnkd.in/e5JBrN4
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April 1, 2019
Download the new Unified Analytics for Dummies eBook to learn how companies are bringing together Data Science and Data Engineering to solve more business problems. https://lnkd.in/gwYe6Jp
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β΄οΈ @AI_Python_EN
April 1, 2019
A super interesting paper on image search and multilingual word embeddings.
"Image search using multilingual texts: a cross-modal learning approach between image and text"
https://lnkd.in/eBwwNne
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"Image search using multilingual texts: a cross-modal learning approach between image and text"
https://lnkd.in/eBwwNne
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April 1, 2019
Are Deep Neural Networks Dramatically Overfitted?
deep-neural-networks-
#deepneuralnetworks
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deep-neural-networks-
#deepneuralnetworks
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April 2, 2019
Peltarionβs, Essential Handbook For AI Leaders, is a great little intro to AI for managers.
https://lnkd.in/eE3QjE8
https://lnkd.in/e4Z5Nrm
#freedeeplearningbooks
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https://lnkd.in/eE3QjE8
https://lnkd.in/e4Z5Nrm
#freedeeplearningbooks
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April 2, 2019