Series of articles «Machine Learning Kaggle Competition» in three parts:

1. Part I: https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426
2. Part II: https://towardsdatascience.com/machine-learning-kaggle-competition-part-two-improving-e5b4d61ab4b8
3. Part III: https://towardsdatascience.com/machine-learning-kaggle-competition-part-three-optimization-db04ea415507

#kaggle #guide

friend: yo dude, wanna drink?

me: nah man, that stuff kills brain cells.

friend: you say its killing brain cells but i say its just real life dropout to prevent overfitting

OpenAI bot defeated 5 human top 99.95 percentile DotA 2 players.

https://twitch.tv/openai

#dota #rl #openai

Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation, and DCGAN implemented with tf.keras and eager execution

“Complete code examples for Machine Translation with Attention, Image Captioning, Text Generation…” https://medium.com/tensorflow/complete-code-examples-for-machine-translation-with-attention-image-captioning-text-generation-51663d07a63d

#tensorflow #tutorial

27.23TB of research data in torrents! Includes dataset such as:
- Breast Cancer Cell Segmentation
- Liver Tumor Segmentation
- MRI Lesion Segmentation in Multiple Sclerosis
- Electron Microscopy, Hippocampus
- Digital Surface & Digital Terrain Model

And courses recordings, including:
- Introduction to Computer Science [CS50x] [Harvard] [2018]
- Artificial Intelligence(EDX)
- Richard Feynman's Lectures on Physics (The Messenger Lectures) (🔥)
- [Coursera] Machine Learning (Stanford University) (ml)
- [Coursera] Natural Language Processing (Stanford University) (nlp)
- [Coursera] Neural Networks for Machine Learning (University of Toronto) (neuralnets)

http://academictorrents.com/

#course #torrent #dataset

Kaggle kernel on classifing Russian Troll tweets.

https://www.kaggle.com/kmader/from-hate-speech-to-russian-bot-tweets

#kaggle #kernel #nlp

Teams at #DeepMind and #Moorfields have developed AI technology that can detect eye disease and prioritise patients. 'Clinically applicable deep learning for diagnosis and referral in retinal OCT' has been published online in #NatureMedicine today:
https://www.nature.com/articles/s41591-018-0107-6

#cv #dl

Fast image-to-image translation in the browser. With 3 trained models introduced. Plus a processed dataset of 1000 images for edges2cats translation.

Demo: https://zaidalyafeai.github.io/pix2pix/cats.html
Code: https://github.com/zaidalyafeai/zaidalyafeai.github.io/tree/master/pix2pix

#tf #tensorflow #tfjs #pix2pix #cv

«IEEE’s camera identification challenge — different approach to teaming up»

https://hackernoon.com/ieees-camera-identification-challenge-different-approach-to-teaming-up-28da44dfe635

#cv #dbrain

Neural nets are terrible at arithmetic & counting. If you train one in 1 to 10, it will do okay on 3 + 5 but fail miserably for 1000 + 3000. Resolving this, «Neural Arithmetic Logic Units» can track time, do arithmetic on images of numbers, & extrapolate, providing better results than other architectures.

https://arxiv.org/pdf/1808.00508.pdf

#nn #architecture #concept #deepmind #arithmetic

The Conversational Intelligence Challenge 2 (ConvAI second part) got announced today.

The aim of our competition is to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable.

The most promising team attending will receive a travel grant to attend #NIPS2018

#nlp #dl #dialoguesystem #competition

Stanford engineers have combined two types of computers to create a faster and less energy-intensive image processor for use in autonomous vehicles, security cameras and medical devices.

https://news.stanford.edu/2018/08/17/new-ai-camera-revolutionize-autonomous-vehicles/

Recent Advances for a Better Understanding of Deep Learning − Part I

https://towardsdatascience.com/recent-advances-for-a-better-understanding-of-deep-learning-part-i-5ce34d1cc914

#dl #theory

What is a Generative Adversarial Network?

Another article about how GANs work.

http://hunterheidenreich.com/blog/what-is-a-gan/

#gan #theory #dl

Curious About How To Be A Data Scientist? Hear From A Netflix Data Scientist

Article about how data science #production works. How problem should be defined and how project should be maintained and run.

https://towardsdatascience.com/a-peek-into-a-netflix-data-scientists-day-66bf3dacabb9

First 1e6 integers, represented as binary vectors indicating their prime factors, and laid out using the sparse matrix support in leland_mcinnes's UMAP dimensionality reduction algorithm. This is from a 1000000x78628 (!) binary matrix. Very pretty structure emerges.

"Learning Hierarchical Semantic Image Manipulation through Structured Representations":

https://arxiv.org/abs/1808.07535

#cv #dl

Online ad demand prediction #kaggle competition 1st place summary:
https://www.kaggle.com/c/avito-demand-prediction/discussion/59880

Winner explains how to combine categorical, numerical, image and text features into a single #NN that gets you into top 10 without stacking.