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

✴️ @AI_Python_EN

Wishes for TensorFlow/Keras 🤞

- 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)

✴️ @AI_Python_EN

Want to know why training on small data is the future? And more importantly, why Andrew named his daughter Nova? Learn why in Andrew’s chat with MIT Tech Review's Will Knight at #EmTechDigital 2019: http://bit.ly/2VdaGwO

✴️ @AI_Python_EN

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. l∞-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 adversary’s 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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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
✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

Are Deep Neural Networks Dramatically Overfitted?


deep-neural-networks-

#deepneuralnetworks

✴️ @AI_Python_EN

Peltarion’s, Essential Handbook For AI Leaders, is a great little intro to AI for managers.
https://lnkd.in/eE3QjE8

https://lnkd.in/e4Z5Nrm
#freedeeplearningbooks

✴️ @AI_Python_EN

What happens when a dataset has too many #variables? Here are few possible situations which you might come across:

• You find that most of the variables are correlated.
• You lose patience and decide to run a model on the whole data which returns poor accuracy
• You become indecisive about what to do
• You start thinking of some strategic method to find few important variables

But dealing with such situations isn’t as difficult as it sounds. Statistical techniques such as factor analysis and principal component analysis help to overcome such difficulties. Here's a detailed guide on Principal Component Analysis - a method to extract important variables from a large set of variables available in a #dataset. Read the full article here: https://lnkd.in/fbKgbrh

✴️ @AI_Python_EN

#Dataset list — A list of the biggest datasets for machine learning

🌎 Dataset list

✴️ @AI_Python_EN

The life of a #DataScientist...
✴️ @AI_Python_EN

How to Build a Simple Artificial Neural Network (#ANN)

#ArtificialNeuralNetwork

🌎 Artificial Neural Network

✴️ @AI_Python_EN

If you're bootstrapping yourself into deep learning research, here’s what I would do:
1. FastAI (3m)
2. Personal projects/reproduce papers/consulting (3 - 12m)
3. Flashcard the Deep Learning Book (4-6m)
4. Flashcard ~100 papers in a niche (2m)
5. Publish your first paper (6m)

✴️ @AI_Python_EN

Now is the time to build an AI startup! The ecosystem has matured, the 4 pillars of AI are now freely accessible

1. Data - Kaggle, Google Dataset Search
2. Algorithms - Arxiv, GitHub
3. Compute - Google Colab, Kaggle Kernels
4. Education - School of AI, KhanAcademy, Fast.AI

CS234: Reinforcement Learning | Winter 2019

By Emma Brunskill: https://lnkd.in/eyNjZBR
‏

#DeepLearning #MachineLearning #ReinforcementLearning

✴️ @AI_Python_EN

Amazing project success for #DeepLearning for #Radiologists

This CNN model for breast cancer did screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images).

Accuracy? ~ 90% in predicting whether there is a cancer in the breast, when tested on the screening population.

It was a two-stage training procedure, which allows us to use a very high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels.

Paper on #ArXiv https://lnkd.in/ggj5Z6W
Code: https://lnkd.in/gScbpUs
Explanation: https://lnkd.in/gfa9gzM

#ai #deeplearning #radiology #model #breast #mammography

✴️ @AI_Python_EN

✴️ @AI_Python_EN

An overview for using #R for validated work:

1.) Base R #Validation for #FDA: https://lnkd.in/ep8TRM8

2.) #RStudio IDE Validation: https://lnkd.in/e34FCXn

3.) Evaluating Package Stability

4.) Evaluating Package Dependencies: https://lnkd.in/eniCXgG

5.) Organizing Packages with an Internal Repository: https://lnkd.in/etSGuk4

#rstats

✴️ @AI_Python_EN

We released a new large-scale corpus of English speech derived for TTS; LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

Dataset: http://www.openslr.org/60/

Paper: http://arxiv.org/abs/1904.02882

✴️ @AI_Python_EN