Countdown to NeurIPS 2019 continues... (5th of 8 studies my team will present) A lot of companies ranging from small startups to large corporate giants are releasing Explainable AI toolkits and core features using popular XAI methods like LIME, SHAP, Integrated Gradients, etc. However, one begs the question: Do the explanations provided by these XAI methods really reflect the decisions made by machine learning algorithm? In this study, we introduce a measurable way in an attempt to answer this question, and study some of the most popular XAI methods to see where they stand for deep neural networks. The results may surprise you...
#deeplearning #neurips
https://arxiv.org/abs/1910.07387

❇️ @AI_Python_EN

Vanishing/exploring gradients problem is a well often problem especially when training big networks, so visualizing gradients is a must when training neural networks. Here is the small network's on MNIST dataset gradients flow. A detailed article is on the way to explain many things in deep learning.

#machinelearning #deeplearning #artificialintelligence #computervision #neuralnetwork

❇️ @AI_Python_EN

New State of the Art AI Optimizer: Rectified Adam (RAdam) Improve your AI accuracy instantly versus Adam, and why it works. Blog by Less Wright :

https://medium.com/@lessw/new-state-of-the-art-ai-optimizer-rectified-adam-radam-5d854730807b

#MachineLearning #TensorFlow #Pytorch #DeepLearning

❇️ @AI_Python_EN

Free 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!

Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IoT
- ...and more

https://www.pyimagesearch.com/start-here

Yoshua explains how #DeepLearning has developed in 2019
https://www.youtube.com/watch?v=eKMA1Tscdag

❇️ @AI_Python_EN

Deep Speech, a good #Persian podcsts about #AI

We will talk about #ArtificialIntelligence, #MachineLearning and #DeepLearning news.

https://castbox.fm/channel/Deep-Speech-id2420707?country=us

❇️ @AI_Python_EN

Best of arXiv.org for #AI, #MachineLearning, and #DeepLearning – November 2019

https://bit.ly/36OWsaD

❇️ @AI_Python_EN

Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l

#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop

🔥 As you know ML has proven its importance in many fields, like computer vision, NLP, reinforcement learning, adversarial learning, etc .. Unfortunately, there is a little work to make machine learning accessible for Arabic-speaking people. Arabic language has many complicated features compared to other languages. First, Arabic language is written right to left. Second, it contains many letters that cannot be pronounced by most foreigners like ض ، غ ، ح ، خ، ظ. Moreover, Arabic language contains special characters called Diacritics which are special characters that help readers pronounced words correctly. For instance the statement السَّلامُ عَلَيْكُمْ وَرَحْمَةُ اللَّهِ وَبَرَكَاتُهُ containts special characters after most of the letters. The diactrics follow special rules to be given to a certain character. These rules are construct a complete area called النَّحْوُ الْعَرَبِيُّ. Compared to English, the Arabic language words letters are mostly connected اللغة as making them disconnected is difficult to read ا ل ل غ ة. ArbML helps fixing this by implementing many open-source projects that support Arabic, ML and NLP.

https://github.com/zaidalyafeai/ARBML

#machinelearning #deeplearning #artificialintelligence #nlp

❇️ @AI_Python_EN

Deep Learning Has Limits. But Its Commercial Impact Has Just Begun.

Rob Toews, Forbes: https://lnkd.in/e6MS8tu

#ArtificialIntelligence #Business #DeepLearning

[MobiNetV1] Removing people from complex backgrounds in real time using TensorFlow.js in the web browser!

This code attempts to learn over time the makeup of the background of a video such that the algorithm can attempt to remove any humans from the scene. This is all happening in real time, in the browser, using TensorFlow.js.


https://lnkd.in/gsePqBH

#deeplearning #machinelearning #artificialintelligence

❇️ @AI_Python_EN

Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner.

abs: https://arxiv.org/abs/2002.05534v1

#rnn #machinelearning #ArtificialIntelligence #DeepLearning #

❇️ @AI_Python_EN

Introduction to Reinforcement Learning

By DeepMind : https://lnkd.in/dd2VNhH

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Access 2 new free online courses
as of today on edXOnline
It's time to hone your #digitalintelligence knowledge and skills, even more if you're getting bored at home:
http://bit.ly/2WLF58R

#DeepLearning

❇️ @AI_Python_EN

Contributor Derrick Mwiti with an overview of #TensorFlow MLIR—a mult-level intermediate representation designed to be a reusable and extensible compiler that works across the #DeepLearning landscape.
https://bit.ly/2Jt83T7

❇️ @AI_Python_EN

Breast cancer classification with Keras and Deep Learning

To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.

Researcher: Adrian Rosebrock
Paper & codes : http://ow.ly/yngq30qjLye

#artificialintelligence #ai #machinelearning #deeplearning #bigdata #datascience

❇️ @AI_Python_EN

Acme: A new framework for distributed reinforcement learning by DeepMind
Intro:
https://deepmind.com/research/publications/Acme
Paper:
https://github.com/deepmind/acme/blob/master/paper.pdf
Repo:
https://github.com/deepmind/acme

#reinforcementlearning #ai #deepmind #deeplearning #machinelearning

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

#TFHub tutorials TensorFlow Hub is a library for reusable machine learning modules : https://lnkd.in/eYk83J5 #DeepLearning #Tensorflow #TensorFlowHub #Tutorials

Big GANs Are Watching

You It is the state-of-the-art unsupervised GAN, which parameters are publicly available. They demonstrate that object saliency masks for GAN-produced images can be obtained automatically with BigBiGAN. These masks then are used to train a discriminative segmentation model. Being very simple and easy-to-reproduce, our approach provides competitive performance on common benchmarks in the unsupervised scenario.

Github: https://github.com/anvoynov/BigGANsAreWatching

Paper : https://arxiv.org/abs/2006.04988

#datascience #machinelearning #artificialintelligence #deeplearning