ββWhy Financial Planning is Excitingβ¦ At Least for a Data Scientist
Great introduction into the finance world and what data scientist can lack diving into the topic.
Link: https://eng.uber.com/financial-planning-for-data-scientist/
#Financial #statistics #Uber
Great introduction into the finance world and what data scientist can lack diving into the topic.
Link: https://eng.uber.com/financial-planning-for-data-scientist/
#Financial #statistics #Uber
ββIBM aiming for 1000x improvement in AI computation over the next 10 years.
How DS / AI sphere will develop according to #IBM prediction.
Link: https://www.nextbigfuture.com/2019/02/ibm-investing-2-billion-in-an-ai-center-and-targets-1000-times-improvement-by-2029.html
How DS / AI sphere will develop according to #IBM prediction.
Link: https://www.nextbigfuture.com/2019/02/ibm-investing-2-billion-in-an-ai-center-and-targets-1000-times-improvement-by-2029.html
ββTOWARDS FEDERATED LEARNING AT SCALE: SYSTEM DESIGN
Google published how they do #FederatedLearning at scale on tens of millions of mobile phones. This is about training model on decentralized data.
ArXiV: https://arxiv.org/pdf/1902.01046.pdf
#Google #Privacy
Google published how they do #FederatedLearning at scale on tens of millions of mobile phones. This is about training model on decentralized data.
ArXiV: https://arxiv.org/pdf/1902.01046.pdf
#Google #Privacy
ββA Style-Based Generator Architecture for Generative Adversarial Networks
Code and pre-trained models for Style-GAN paper.
Github: https://github.com/NVlabs/stylegan
ArXiV: http://stylegan.xyz/paper
#GAN #CV #DL
Code and pre-trained models for Style-GAN paper.
Github: https://github.com/NVlabs/stylegan
ArXiV: http://stylegan.xyz/paper
#GAN #CV #DL
ββIntuitive Visualization of Outlier Detection Methods
Mostly with Python Outlier Detection.
Link: https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html
#outliers #dataviz #visualization
Mostly with Python Outlier Detection.
Link: https://www.kdnuggets.com/2019/02/outlier-detection-methods-cheat-sheet.html
#outliers #dataviz #visualization
ββAdvanced Technologies for Detecting and Preventing Fraud at Uber
Uberβs article on how they detect and prevent fraud, analyzing GPS traces and usage patterns to identify suspicious behavior.
Link: https://eng.uber.com/advanced-technologies-detecting-preventing-fraud-uber/
#geodata #Uber #fraud #GPS
Uberβs article on how they detect and prevent fraud, analyzing GPS traces and usage patterns to identify suspicious behavior.
Link: https://eng.uber.com/advanced-technologies-detecting-preventing-fraud-uber/
#geodata #Uber #fraud #GPS
Itβs better to be late, than to miss amazing opportunite to take part in not-official, but supervised online course of Deep Learning by CMU.
Course has already started, but you still can hop on the departing train to learn (or review) deep learning foundations and have some practice.
Content, quizes, practice exercises are in English.
This initiative was brought by editor of russian-speaking @powerofdata channel.
Link: https://dlcourse.ru
Original course page (with mention of aforementioned initiative): http://deeplearning.cs.cmu.edu
Course has already started, but you still can hop on the departing train to learn (or review) deep learning foundations and have some practice.
Content, quizes, practice exercises are in English.
This initiative was brought by editor of russian-speaking @powerofdata channel.
Link: https://dlcourse.ru
Original course page (with mention of aforementioned initiative): http://deeplearning.cs.cmu.edu
A new ELF OpenGo bot and analysis of historical Go games
Facebook AI Research shared new features & research results related to ELF OpenGo, including an updated model that was retrained from scratch. Bonus: Windows executable version of the bot, and a unique archive analyzing 87K professional Go games.
Link: https://ai.facebook.com/blog/open-sourcing-new-elf-opengo-bot-and-go-research/
#facebook #gogame
Facebook AI Research shared new features & research results related to ELF OpenGo, including an updated model that was retrained from scratch. Bonus: Windows executable version of the bot, and a unique archive analyzing 87K professional Go games.
Link: https://ai.facebook.com/blog/open-sourcing-new-elf-opengo-bot-and-go-research/
#facebook #gogame
Data Science by ODS.ai π¦
ββA Style-Based Generator Architecture for Generative Adversarial Networks Code and pre-trained models for Style-GAN paper. Github: https://github.com/NVlabs/stylegan ArXiV: http://stylegan.xyz/paper #GAN #CV #DL
Application of #StyleGAN: site, which generates new people every page refresh.
Link: https://thispersondoesnotexist.com
Link: https://thispersondoesnotexist.com
OpenAIβs new model can generate surprisingly realistic fake news.
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization β all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
New model, called GPT-2 is an unsupervised language model that can generate coherent paragraphs and perform rudimentary reading comprehension, machine translation, question answering, and summarization β all without task-specific training.
Link: https://blog.openai.com/better-language-models/
Paper: https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
#OpenAI #NLP #fakenews #qa #DL
Data Science by ODS.ai π¦
Neural networks #architecture #cheatsheet #nn
One of the most complete lists of #NN types.
Link: http://www.asimovinstitute.org/neural-network-zoo/
Link: http://www.asimovinstitute.org/neural-network-zoo/
The Asimov Institute
The Neural Network Zoo - The Asimov Institute
With new neural network architectures popping up every now and then, itβs hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheatβ¦
ββObject Detection Networks and Augmented Reality for Cellular Detection in Fluorescence Microscopy Acquisition and Analysis.
#DL automated microscopy with objection detection.
Paper: https://www.biorxiv.org/content/10.1101/544833v1
#AugmentedReality
#DL automated microscopy with objection detection.
Paper: https://www.biorxiv.org/content/10.1101/544833v1
#AugmentedReality
ββSC-FEGAN: Face Editing Generative Adversarial Network with Userβs Sketch and Color
New paper on architecture that lets you add/change: earrings, glasses, hair style, dimples, & more. Sketches are trasformed to the photo by #GAN network.
ArXiV: https://arxiv.org/pdf/1902.06838.pdf
Code: https://github.com/JoYoungjoo/SC-FEGAN
New paper on architecture that lets you add/change: earrings, glasses, hair style, dimples, & more. Sketches are trasformed to the photo by #GAN network.
ArXiV: https://arxiv.org/pdf/1902.06838.pdf
Code: https://github.com/JoYoungjoo/SC-FEGAN
"Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet"
A "bag of words" of nets on tiny 17x17 patches suffice to reach AlexNet-level performance on ImageNet. A lot of the information is very local.
Paper: https://openreview.net/forum?id=SkfMWhAqYQ
#fun #CNN #CV #ImageNet
A "bag of words" of nets on tiny 17x17 patches suffice to reach AlexNet-level performance on ImageNet. A lot of the information is very local.
Paper: https://openreview.net/forum?id=SkfMWhAqYQ
#fun #CNN #CV #ImageNet
OpenReview
Approximating CNNs with Bag-of-local-Features models works...
Aggregating class evidence from many small image patches suffices to solve ImageNet, yields more interpretable models and can explain aspects of the decision-making of popular DNNs.
Probabilistic foundations of econometrica: part 1
Great intro into #statistics basics.
Link: https://freakonometrics.hypotheses.org/57649
#beginner #novice #entrylevel
Great intro into #statistics basics.
Link: https://freakonometrics.hypotheses.org/57649
#beginner #novice #entrylevel
Freakonometrics
Probabilistic Foundations of Econometrics, part 1
In a series of posts, I wanted to get into details of the history and foundations of econometric and machine learning models. It will be some sort of online version of our joint paper with Emmanuel Flachaire and Antoine Ly, Econometrics and Machine Learningβ¦
Analyzing Experiment Outcomes: Beyond Average Treatment Effects
Good #statistics article on why tail distribution and #experimentdesign matters. Quantile treatment effects (QTEs) helps to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the #Uber marketplace.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
Good #statistics article on why tail distribution and #experimentdesign matters. Quantile treatment effects (QTEs) helps to capture the inherent heterogeneity in treatment effects when riders and drivers interact within the #Uber marketplace.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
ββPseudo-extended Markov chain Monte Carlo
Pseudo-Extended #MC for easier sampling from multimodal posteriors. Extend the target distribution and then run your favourite sampler (f.e. #HMC).
ArXiV: https://arxiv.org/abs/1708.05239
#statistics
Pseudo-Extended #MC for easier sampling from multimodal posteriors. Extend the target distribution and then run your favourite sampler (f.e. #HMC).
ArXiV: https://arxiv.org/abs/1708.05239
#statistics
ββWeakly supervised mitosis detection in breast histopathology images using concentric loss
Weakly-supervised mitosis detection in breast histopathology images shows that only using one-click annotation can obtain the best performances on three challenging datasets.
Link: https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118?dgcid=author
#healthcare #medical #CV #cancer #DL
Weakly-supervised mitosis detection in breast histopathology images shows that only using one-click annotation can obtain the best performances on three challenging datasets.
Link: https://www.sciencedirect.com/science/article/abs/pii/S1361841519300118?dgcid=author
#healthcare #medical #CV #cancer #DL
ββLearning to Generalize from Sparse and Underspecified Rewards
Applying reinforcement learning to environments with sparse and underspecified rewards is an ongoing challenge, requiring generalization from limited feedback. Novel method that provides more refined feedback to the agent.
Link: https://ai.googleblog.com/2019/02/learning-to-generalize-from-sparse-and.html
#Google #RL
Applying reinforcement learning to environments with sparse and underspecified rewards is an ongoing challenge, requiring generalization from limited feedback. Novel method that provides more refined feedback to the agent.
Link: https://ai.googleblog.com/2019/02/learning-to-generalize-from-sparse-and.html
#Google #RL
