It's out! I really think the network portrait divergence is a clever way to compare two networks--props to Jim Bagrow & Bollt for introducing it.

Paper:
https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0156-x

Code:
https://github.com/bagrow/network-portrait-divergence

✴️ @AI_Python_EN

Remember the #BachDoodle? We’re excited to release paper on Behind-the-Scenes design, #ML, scaling it up, and dataset of 21.6M melodies from around the world!
📜 http://arxiv.org/abs/1907.06637

✴️ @AI_Python_EN

This #AI magically removes

moving #objects from #videos

https://buff.ly/2XJtPf3
#ArtificialIntelligence #MachineLearning #DeepLearning
Fast Online Object Tracking and Segmentation: A Unifying Approach
✴️ @AI_Python_EN

For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing
7. Cognitive Neuroscience
8. Research Papers and Related Courses

https://t.me/DeepLearningML

Deep Learning for NLP: An Overview of Recent Trends


1) Word Embeddings
2) Character Embeddings
3) Convolutional Neural Network (CNN)
4) Recurrent Neural Network (RNN)
5) Attention Mechanim
6) Recursive Neural Network
and more briefly trends!!
https://medium.com/dair-ai/deep-learning-for-nlp-an-overview-of-recent-trends-d0d8f40a776d




also a good start in Keras for beginners using BiLSTM, CNN, ...
link —> https://github.com/BrambleXu/nlp-beginner-guide-keras




✴️ @AI_Python_EN

A gentle overview on the Deep Learning and Machine Learning

The Deep Learning is a subarea of the Machine Learning that makes use of Deep Neural Networks (with many layers) and specific novel algorithms for the pre-processing of data and regularization of the model: word embeddings, dropout, data-augmentation. Deep Learning takes inspiration from Neuroscience since Neural Networks are a model of the neuronal network within the brain. Unlike the biological brain, where any neuron can connect to any other under some physical constraints, Artificial Neural Networks (ANNs) have a finite number of layers, connections, and fixed direction of the data propagation. So far, ANNs have been ignored by the research and business community. The problem is their computational cost.

Between 2006 and 2012 the research group led by Geoffrey Hinton at the University of Toronto finally parallelized the ANNs algorithms to parallel architectures. The main breakthroughs are the increased number of layers, neurons, and model parameters in general (even over than 10 million) allowing to compute massive amounts of data through the system to train it.

https://lnkd.in/dq87iFy

#neuralnetwork
#deeplearning
#machinelearning

✴️ @AI_Python_EN

Second Fashion-Gen challenge at #ICCV2019:

Fashion-Gen is a dataset of ~300K images paired with item descriptions. The task is image generation conditioned on the given text descriptions.
Deadline: Oct 15
Challenge:
https://fashion-gen.com
Paper:
https://arxiv.org/pdf/1806.08317

✴️ @AI_Python_EN

This Series of FREE PYTHON TUTORIALS will help You to get Closer to your Data Science Dream 💯🔥🔝



https://data-flair.training/blogs/python-tutorials-home/

✴️ @AI_Python_EN

List of institutions with most accepted papers at NeurIPS.
Github code for this graph :
https://lnkd.in/edwhZMf
Medium Link:
https://medium.com/@dcharrezt/neurips-2019-stats-c91346d31c8f

✴️ @AI_Python_EN

Introducing Neural Structured Learning in TensorFlow

https://medium.com/tensorflow/introducing-neural-structured-learning-in-tensorflow-5a802efd7afd

Neural Structured Learning: Training with Structured Signals

Article: https://www.tensorflow.org/neural_structured_learning

Code: https://github.com/tensorflow/neural-structured-learning

✴️ @AI_Python_EN

How Humans Judge Machines? Brain Bar presentation on my forthcoming book in this topic, scheduled for the spring of 2020.
https://www.youtube.com/watch?v=NQQ2CEqBe1Y

ideo Interpolation and Prediction with Unsupervised Landmarks

Shih et al.: https://lnkd.in/d2A3juW

#ArtificialIntelligence #DeepLearning
#MachineLearning

✴️ @AI_Python_EN

Basics of Python Programming
——————————————-
a. Lists, Tuples, Dictionaries, Conditionals, Loops, etc...
https://lnkd.in/gWRbc3J
b. Data Structures & Algorithms
https://lnkd.in/gYKnJWN
d. NumPy Arrays:
https://lnkd.in/geeFePh
c. Regex:
https://lnkd.in/gzUahNV

Practice Coding Challenges
—————————————
a. Hacker Rank:
https://lnkd.in/gEufBUu
b. Codeacademy:
https://lnkd.in/gGQ7cuv
c. LeetCode:
https://leetcode.com/

Data Manipulation
————————-
a. Pandas:
https://lnkd.in/gxSgfuQ
b. Pandas Cheatsheet:
https://lnkd.in/gfAdcpw
c. SQLAlchemy:
https://lnkd.in/gjvbm7h

Data Visualization
————————
a. Matplotlib:
https://lnkd.in/g_3fx_6
b. Seaborn:
https://lnkd.in/gih7hqz
c. Plotly:
https://lnkd.in/gBYBMXc
d. Python Graph Gallery:
https://lnkd.in/gdGe-ef

Machine Learning / Deep Learning
————————————————
a. Skcikit-Learn Tutorial:
https://lnkd.in/gT5nNwS
b. Deep Learning Tutorial:
https://lnkd.in/gHKWM5m
c. Kaggle Kernels:
https://lnkd.in/e_VcNpk
d. Kaggle Competitions:
https://lnkd.in/epb9c8N

Has Area Under the ROC Curve (AUC-ROC) become Data Science & AI/ML community’s P-Value?

Just returned from day 1 of Intelligent Health AI conference - and while there were some great speakers & talks - one thing stood out. Of the multiple talks reporting machine learning model performance, all except one talk reported AUC-ROC as the only metric - even for unbalanced datasets. It appears that the AUC-ROC metric is being misused similar to how the P-value has been misused & misinterpreted.

There is more to model evaluation than a single number. In addition to AUC-ROC, we have the Precision-Recall (PR) curve, Sensitivity (Recall), Specificity, F1-score, Positive/Negative Predictive Values, Matthews Correlation Coefficient, Calibration, and many other metrics. The graphic below presents a good summary of the various model performance / evaluation metrics (see articles & book for more details):

Regression Metrics:
https://lnkd.in/eRWvRVc

Classification Metrics:
https://lnkd.in/dpYnvGh

Evaluating Machine Learning Models (open-access book):
https://lnkd.in/dHcfZdP

Meta-Learning with Implicit Gradients

Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://lnkd.in/g9H6mZ2

#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning