Optimizing Millions of Hyperparameters by Implicit Differentiation Lorraine et al.:

https://arxiv.org/abs/1911.02590
#ArtificialIntelligence #MachineLearning

❇️ #AI_Python_EN

Story Realization: Expanding Plot Events into Sentences Ammanabrolu et al.:

https://arxiv.org/abs/1909.03480

#ArtificialIntelligence #DeepLearning #MachineLearning

❇️ @AI_Python_EN

News classification using classic Machine Learning tools (TF-IDF) and modern NLP approach based on transfer learning (ULMFIT) deployed on GCP
Github:
https://github.com/imadelh/NLP-news-classification

Blog:
https://imadelhanafi.com/posts/text_classification_ulmfit/

#DeepLearning #MachineLearning #NLP

❇️ @AI_Python_EN

Research Guide: Advanced Loss Functions for Machine Learning Models

http://bit.ly/36HBefu

#DataScience #MachineLearning #ArtificialIntelligence

❇️ @AI_Python_EN

Intro to optimization in deep learning: Momentum, RMSProp and Adam

https://bit.ly/2zwBLV0

❇️ @AI_Python_EN

A list of the biggest machine learning datasets from across the web

https://bit.ly/2TYGdVD

❇️ @AI_Python_EN

Self-training with Noisy Student improves ImageNet classification Xie et al.:

https://arxiv.org/abs/1911.04252

#ArtificialIntelligence #DeepLearning #MachineLearning

❇️ @AI_PythonEN

Productionizing #NLP Models

https://bit.ly/2OkdRAD

❇️ @AI_Python_EN

Correlation Coefficients in One Picture

❇️ @AI_PYTHON_EN

Memory Augmented Recursive Neural Networks
Arabshahi et al.:

https://arxiv.org/abs/1911.01545

#ArtificialIntelligence #MachineLearning #NeuralNetworks

❇️ @AI_Python_EN

Visualizing an AI model’s blind spots

http://bit.ly/2CosFZn

#DataScience #MachineLearning #ArtificialIntelligence

❇️ @AI_PYTHON_EN

"SEMINAL DEBATE : YOSHUA BENGIO | GARY MARCUS" This Is The Debate The AI World Has Been Waiting For LIVE STREAMING :

https://www.eventbrite.ca/e/seminal-debate-yoshua-bengio-gary-marcus-live-streaming-tickets-81620778947

Date and Time : December 23, 2019 | 7:00 PM – 8:30 PM EST

#ArtificialIntelligence

❇️ @AI_Python_EN

A free linear algebra #textbook with solutions by Jim Hefferon. This knowledge will be very useful for understanding #machinelearning and beyond.

http://joshua.smcvt.edu/linearalgebra/#current_version

#book

❇️ @AI_Python_EN

Part of the communication challenges between data scientists and the business result from thinking one methodology is going to solve two problems. Illustrative example: The biz asks for a highly predictive churn model (this could be extended to many different use cases, but we're keeping it simple here). In reality, the biz wants to be able to:
1. Accurately identify customers with a high risk of churn so that they can implement some type of corrective measures.
2. They also want recommendations (based on data) that will inform what corrective measures could potentially have the biggest impact on reducing churn. To give the biz what they're expecting, it's possible that you'll need to build two separate models. (one that is highly predictive, the other that is easily interpretable). Bonus, once you've already collected the data, it's not that much incremental effort to build multiple models.
Agree or Disagree? And if you agree, are you already approaching things this way?

❇️ @AI_Python_EN

Teaching a neural network to use a calculator.

https://reiinakano.com/2019/11/12/solving-probability.html

#ArtificialIntelligence #DeepLearning #MachineLearning

❇️ @AI_Python_EN

Machine ignoring = underfitting
Machine learning = optimal fitting
Machine memorization = overfitting

#datascience #machinelearning

❇️ @AI_Python_EN

How to Read Articles That Use Machine Learning Users’ Guides to the Medical Literature

https://jamanetwork.com/journals/jama/article-abstract/2754798

#machinelearning #paper #ArtificialIntelligence

❇️ @AI_Python_EN

A must read document for deep learning & machine learning practitioners

https://www.deeplearningbook.org/contents/guidelines.html

#deeplearning #machinelearning

❇️ @AI_Python_EN

74 Summaries of Machine Learning and NLP Research Marek Rei :

http://www.marekrei.com/blog/74-summaries-of-machine-learning-and-nlp-research/

#ArtificialIntelligence #DeepLearning #MachineLearning

❇️ @AI_Python_EN

Looking for Masters and PhD level students for Paylocity's data science internship program! Students must be in their penultimate year of school, with strong knowledge of machine learning and software engineering. You'll work with Paylocity's incredible talented Product Owners to translate our customers' business needs into data science needs and deliver features that enable next generation HR analytics.

https://2000recruiting.paylocity.com/recruiting/jobs/Details/2767/Paylocity/Data-Scientist-Intern---Summer-2020

❇️ @AI_Python_EN