I try to make the interview a fun moment for programmers.
https://lnkd.in/dJJbFbE

OpenAI's MuseNet Learned to Compose Mozart, Bon Jovi and More

article : https://openai.com/blog/musenet/
video : https://www.youtube.com/watch?v=pQA8Wzt8wdw

article on Machine learning got published in Better Programming on Medium
https://medium.com/better-programming/from-machine-learning-to-reinforcement-learning-mastery-47f33d9f6b41

Feedback welcome!

✴️ @AI_Python_EN

"What Does BERT Look At? An Analysis of BERT's Attention"
https://arxiv.org/abs/1906.04341

✴️ @AI_Python_EN

New feature in Habitat-API: tensorboard integration and video generation: watch videos of your agents at various points in training. Courtesy: Jason Jiazhi Zhang.
https://github.com/facebookresearch/habitat-api/pull/127

✴️ @AI_Python_EN

APS Physics Viewpoint on 4 independent works on Neural Network Variational Methods for Open Quantum Systems!
https://physics.aps.org/articles/v12/74

✴️ @AI_Python_EN

Convolutional #NeuralNetworks (CNN) for Image Classification — a step by step illustrated tutorial: https://dy.si/hMqCH
BigData #AI #MachineLearning #ComputerVision #DataScientists #DataScience #DeepLearning #Algorithms

✴️ @AI_Python_EN

Modern machine learning is driven by building good environments/datasets. We’ve just open-sourced a tool we created for rendering high-quality synthetic robotics data:
OpenAI : We're releasing ORRB (OpenAI Remote Rendering Backend)—a Unity3d-based system that enables rapid and customizable renderings of robotics environments.
Paper: https://arxiv.org/abs/1906.11633
Code: https://github.com/openai/orrb

✴️ @AI_Python_EN

#Python, Performance, and GPUs

https://towardsdatascience.com/python-performance-and-gpus-1be860ffd58d

✴️ @AI_Python_EN

Setting the standard for #machinelearning
https://phys.org/news/2019-06-standard-machine.html

✴️ @AI_Python_EN

Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
arxiv.org/abs/1805.06299

#NLP #MachineLearning #DeepLearning

✴️ @AI_Python_EN

I am a big fan of small data for big discovery in #DataScience:
https://mapr.com/blog/when-big-data-goes-local-small-data-gets-big-part-2/

———
But #DeepLearning needs large labeled training sets of #BigData:
https://hackernoon.com/%EF%B8%8F-big-challenge-in-deep-learning-training-data-31a88b97b282

———

Human-Machine Collaborative Annotation with #MachineLearning can help:
https://onlinelibrary.wiley.com/doi/full/10.1002/bult.2013.1720390414

✴️ @AI_Python_EN

Getting System Information in Linux using Python Script.
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux

http://bit.ly/2X56cZa

✴️ @AI_Python_EN

The latest Machine Learning Daily!
https://paper.li/MachineCoding/1370791453?edition_id=51a63f80-99b9-11e9-a7d8-0cc47a0d15fd

Skill from Video data + motion reconstruction
code: https://github.com/akanazawa/motion_reconstruction
CVPR'19 human motion paper training
code: https://github.com/akanazawa/human_dynamics

✴️ @AI_Python_EN

StarAi: FREE Deep Reinforcement Learning Course

https://www.starai.io/course/

✴️ @AI_Python_EN

By the creator of Keras.

#machinelearning #datascience #stats #math

✴️ @AI_Python_EN

All you need to know about Classification and Regression (Machine Learning in 270 pages)

In classification problems, we are trying to predict a discrete number of values. The labels(y) generally comes in the categorical form and represents a finite number of classes.

+Decision Trees
+Logistic Regression
+Naive Bayes
+K Nearest Neighbors
+Linear SVC (Support vector Classifier)s.

In regression problems, we try to predict continuous valued output, take this example. Given the size of the house predict the price(real value).

+Regression Algorithms
+Linear Regression
+Regression Trees(e.g. Random Forest)
+Support Vector Regression (SVR)

✴️ @AI_Python_EN

Does text recognition stuff excite you?
Or do you use google translate which makes you think 'Damn,this is magic!'

This is what I used to feel but after research and #positive approach in learning I landed up getting my basics clear for NLP. Sharing the same with you.

Natural Language Processing is no rocket science and a sub-field of computer science and AI that enables computers to understand and process human language.

What is a word?
-a sequence of meaningful characters, words can be silly as well,DUH ._.

And how it goes around??(basicsteps)
-some sequence of steps(just giving you a jiffy,rest well explained in the pdf)
-Tokenization
-Token Normalisation
-Stemming
-Lemmatization

Attaching a beautifuI research paper from arXiv,give it a read and start with your fun journey.
#datascience #nlp
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

Learn:

- Practical Deep Learning http://course.fast.ai/
- Deep Learning Foundations https://lnkd.in/dhJJYhw
- Computational Linear Algebra https://lnkd.in/e3zAvzF
- Intro Machine Learning http://course.fast.ai/ml

#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN
🗣 @AI_Python_arXiv

Is this really a paradox as claimed by the authors? Because of small sample sizes, once 2-year data are in why don't we just ignore the individual yearly baseball performance figures?
http://qualitysafety.bmj.com/content/23/9/701

✴️ @AI_Python_EN
🗣 @AI_Python_arXiv