Data Science Life Cycle


#artificialintellegence #machinelearning

❇️ @AI_Python_EN

Introduction to Autoencoders - Unsupervised Deep Learning Models (Cont'd) | Coursera

https://bit.ly/2Nw5CCh

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Can We Learn the Language of Proteins?

#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/33jCof6

❇️ @AI_Python_EN

What if you can generate a whole new Image just by giving its textual description?

Learn for Shibsankar Das in his hack session here:
http://bit.ly/DHS2019_66

He’ll be talking about “Generating Synthetic Images from Textual Description using GANs” in which he’ll implement GANs from scratch, formulate business use-cases

❇️ @AI_Python_EN

A neural network that transforms a design mock-up into a static website

https://github.com/emilwallner/Screenshot-to-code

#ArtificialIntelligence #DeepLearning #MachineLearning

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

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

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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

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

https://bit.ly/2zwBLV0

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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

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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