Intel AI Interview Questions — Acing the AI Interview
https://medium.com/acing-ai/intel-ai-interview-questions-acing-the-ai-interview-d4a62dbb593c

Google launches new search engine to help scientists find the datasets they need


https://www.theverge.com/2018/9/5/17822562/google-dataset-search-service-scholar-scientific-journal-open-data-access

Analysis of the tonality of texts using convolutional neural networks
https://habr.com/company/mailru/blog/417767/

Conceptual Captions: A New Dataset and Challenge for Image Captioning https://ai.googleblog.com/2018/09/conceptual-captions-new-dataset-and.html

Lite Intro into Reinforcement Learning https://towardsdatascience.com/lite-intro-into-reinforcement-learning-857ca5c924d9?gi=692d05ca6c3d

Simple introduction to the ALU for neural networks: explanation, physical meaning and implementation https://habr.com/post/422777/

Healthcare Dataset with #PySpark

Back to some #BigData stuff: article about quick-and-dirty development of prediction algorithm using #Spark engine.

Link: https://towardsdatascience.com/healthcare-dataset-with-spark-6bf48019892b

Highlights from the European Conference on Computer Vision 2018 https://blog.alookanalytics.com/2018/09/12/highlights-from-the-european-conference-on-computer-vision-2018/

GMM-UBM и алгоритма MAP adaptation https://habr.com/post/420515/

Introducing the Unrestricted Adversarial Examples Challenge https://ai.googleblog.com/2018/09/introducing-unrestricted-adversarial.html

How to Model Human Activity From Smartphone Data https://machinelearningmastery.com/how-to-model-human-activity-from-smartphone-data/

A new understanding of Batch Normalization https://medium.com/@geetankr/a-new-understanding-of-batch-normalization-e11e05e120dc

Machine Learning Crash Course
with TensorFlow APIs https://developers.google.com/machine-learning/crash-course/

deep learning object detection

A paper list of object detection using deep learning.

https://github.com/hoya012/deep_learning_object_detection

How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/

Next-gen AI: techniques https://becominghuman.ai/next-gen-ai-techniques-e1f7033ce25c

Geoffrey Hinton: The Foundations of Deep Learning https://www.youtube.com/watch?v=zl99IZvW7rE

PyTorch — Python + Nim

https://medium.com/@giovanni_94706/introducing-nimtorch-b8b0fa749464

Two Sigma: Using News to Predict Stock Movements

Use news analytics to predict stock price performance

1st place - $25,000
2nd place - $20,000
3rd place - $15,000
4th through 7th place - $10,000 each

This is a two-stage competition consisting of a Submission period and a Scoring period. In the Submission period, entrants will train their models in Kaggle Kernels. During the Scoring period, models submitted at the end of the submission period will be evaluated against regularly updated news and market data.

Start Date: 9/25/2018
Rules Acceptance/Team Merger Deadline: 1/2/2019
Submission Deadline: 1/8/2019 After this date, we will not be taking any more submissions. You can sit back and watch the leaderboard unfold. Remember to select your two best submissions to be rescored. In this competition we will not auto-select your two submissions.
End Date: 7/15/2019

https://www.kaggle.com/c/two-sigma-financial-news