Which doodles are human-drawn and which are AI-generated? Berkeley researchers Forrest Huang et al created a #neuralnetwork that can generate sketches based on text descriptions:
http://bit.ly/2LZSHJN


✴️ @AI_Python_EN

The basics of NLP and real time sentiment analysis with open source tools

#NLP #sentimentanalysis #opensource

The basics of NLP and real time sentiment analysis with open source tools
✴️ @AI_Python_EN

Quant types often dismiss qualitative research as subjective and, therefore, unscientific.

Though a quant type myself, I must dissent. Not on the grounds that qualitative research is purely objective, but on the grounds that most quantitative research in any field requires a substantial amount of subjective judgment on the part of the research team, statisticians included.

Were this not the case, there would be little need to reproduce and replicate quantitative research findings, apart from concerns about fraud.

There would be far fewer journal articles, books and conferences, and little need for #meta_analysis. All #AI would give us the same answers, too.

✴️ @AI_Python_EN

letting beginners and experts alike learn about SAP HANA.
Download here --> https://lnkd.in/eTtdvi4

End to end Machine learning platform.
Bring your own language and microservices.Java, Node.js and Python are the officially supported languages.

SAP HANA is an ACID-compliant database and application development platform. You can use advanced data processing capabilities—text, graph, spatial, predictive, and more—to pull insights from all types of data.

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #python #R #java #SQL

✴️ @AI_Python_EN

Data-Efficient Image Recognition with Contrastive Predictive Coding

Hénaff et al.: https://lnkd.in/eMDhrU8

#ArtificialIntelligence #ComputerVision #MachineLearning

✴️ @AI_Python_EN

Full Stack Deep Learning Bootcamp
(Most of) Lectures of Day 1:
https://fullstackdeeplearning.com/march2019

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

Probabilistic Graphical Models

Spring 2019 • Carnegie Mellon University

Lisa Lee
https://sailinglab.github.io/pgm-spring-2019/lectures/

✴️ @AI_Python_EN

Deep Learning lecture
The full deck of (600+) slides, by Gilles Louppe:
https://glouppe.github.io/info8010-deep-learning/pdf/lec-all.pdf

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.

SOD - An Embedded #ComputerVision & #MachineLearning Library
https://sod.pixlab.io/
api https://sod.pixlab.io/api.html
samples https://sod.pixlab.io/samples.html
guide https://sod.pixlab.io/intro.html
github https://github.com/symisc/sod

✴️ @AI_Python_EN

Moving Camera, Moving People: A #DeepLearning Approach to Depth Prediction

http://ai.googleblog.com/2019/05/moving-camera-moving-people-deep.html

✴️ @AI_Python_EN

"Storytelling with Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic

I've just come across this first (2015) edition, and there now may be a second edition out. Here's the link to the PDF:

https://lnkd.in/fJSN7ci

✴️ @AI_Python_EN

Estimators, Loss Functions, Optimizers —Core of ML Algorithms

https://towardsdatascience.com/estimators-loss-functions-optimizers-core-of-ml-algorithms-d603f6b0161a]

✴️ @AI_Python_EN

Bringing human-like reasoning to driverless car navigation
Autonomous control system “learns” to use simple maps and image data to navigate new, complex routes.
#COMPUTERVISION

http://news.mit.edu/2019/human-reasoning-ai-driverless-car-navigation-0523

✴️ @AI_Python_EN

torchvision 0.3.0: segmentation, detection models, new datasets, C++/CUDA operators Blog with link to tutorial, release notes: https://pytorch.org/blog/torchvision03/ … Install commands have changed, use the selector on https://pytorch.org

NEW VIDEO: Learn how to write better, more efficient #pandas code 🐼 📺 https://www.youtube.com/watch?v=dPwLlJkSHLo … Download the dataset to follow along with the exercises: 👩‍💻 https://github.com/justmarkham/pycon-2019-tutorial … Become more fluent at using pandas to answer your own #DataScience questions! #Python

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters. (arXiv:1905.09550v1 [http://stat.ML ]) http://bit.ly/2JyBy8

✴️ @AI_Python_EN

ERT Rediscovers the Classical NLP Pipeline by I. Tenney, D. Das & E. Pavlic is 4 pages of great insights https://arxiv.org/abs/1905.05950 Such a constant source of fascinating papers from Ellie Pavlick & her collaborators! Here's BERT correcting his prediction along the model depth🤯

Awesome research from Google #RL team. Learning dynamics from video
https://planetrl.github.io

✴️ @AI_Python_EN

The other day, someone asked me how many kinds of regression there were.

OLS and binary logistic are very popular. There are also numerous methods for count data, ordinal data, as well as multinomial models.

There are many kinds of time-to-event models, and nonparametric forms of regression.

We also have polynomial, spline and quantile regression. Some consider ridge, lasso and elastic net as separate types of regression.

IV regression, SUR, Tobit, Heckit, and hurdle regression are some other kinds which are widely-used. 

Neural Nets, arguably, are a form of regression. There are also many ways to estimate the same regression model with likelihood and Bayesian approaches.

In addition, there are methods designed for time-series analysis and longitudinal modeling, as well as hierarchical, mixed and mixture models.

I've only scratched the surface, and new methods are continually being developed.

I should note that I've used all the methods mentioned above. Each has practical value for hard hat statisticians like me and are not merely of academic interest.

✴️ @AI_Python_EN

Understanding Neural Networks via Feature Visualization: A survey

Nguyen et al.: https://lnkd.in/eRZMuTS

#neuralnetworks #generatornetwork #generativemodels

✴️ @AI_Python_EN

ery interesting paper on machine learning algorithms. This paper compares polynomial regression vs neural networks applying on several well known datasets (including MNIST). The results are worth looking.

Other datasets tested: (1) census data of engineers salaries in Silicon Valley; (2) million song data; (3) concrete strength data; (4) letter recognition data; (5) New York city taxi data; (6) forest cover type data; (7) Harvard/MIT MOOC course completion data; (8) amateur athletic competitions; (9) NCI cancer genomics; (10) MNIST image classification; and (11) United States 2016 Presidential Election.

I haven't reproduced the paper myself but I am very tempted in doing it.

Link here: https://lnkd.in/fd-VNtk

#machinelearning #petroleumengineering #artificialintelligence #data #algorithms #neuralnetworks #predictiveanalytics

✴️ @AI_Python_EN