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

Awesome victory for #DeepLearning 👏🏻

GE Healthcare wins FDA clearance for #algorithms to spot type of collapsed lung!

Here’s how the AI algorithm works
————————————————
1. A patient image scanned on a device is automatically searched for pneumothorax.
2. If pneumothorax is suspected, an alert with the original chest X-ray, is sent to the radiologist to review.
3. That technologist would also receive an on-device notification to highlight prioritized cases.
4. Algorithms would then analyze and flag protocol and field of view errors and auto rotate images on device.

Article is here:
https://lnkd.in/daNYHfP

#machinelearning

Video of the ACM Tech Talk webinar I gave on 2018/07/11.

ACM says this is one of the most popular Tech Talks ever.

https://youtu.be/zikdDOzOpxY

Hierarchical Decision Making by Generating and Following Natural Language Instructions

“Experiments show that models using natural language as a latent variable significantly outperform models that directly imitate human actions.”

https://arxiv.org/abs/1906.00744

Counterfactual Story Reasoning and Generation”, presents the TimeTravel dataset that tests causal reasoning capabilities over natural language narratives.

Paper:
https://arxiv.org/abs/1909.04076
Code+Data:
https://github.com/qkaren/Counterfactual-StoryRW

What Kind of Language Is Hard to Language-Model?

Mielke et al.: https://lnkd.in/eDUGmse

#ArtificialIntelligence #MachineLearning #NLP

CvxNets: Learnable Convex Decomposition

Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, Andrea Tagliasacchi : https://lnkd.in/eGUqxjz

Facebook Research at Interspeech 2019

https://ai.facebook.com/blog/facebook-research-at-interspeech-2019/

Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

https://research.fb.com/publications/sequence-to-sequence-speech-recognition-with-time-depth-separable-convolutions/

Unsupervised Singing Voice Conversion

https://research.fb.com/publications/unsupervised-singing-voice-conversion/

The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.


code: https://github.com/salesforce/ctrl

article: https://einstein.ai/presentations/ctrl.pdf

https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/

What makes a good conversation?
How controllable attributes affect human judgments

A great post on conversation scoring.

Link:
http://www.abigailsee.com/2019/08/13/what-makes-a-good-conversation.html
Paper:
https://www.aclweb.org/anthology/N19-1170

#NLP #NLU #DL

❇️ @ai_python_en