Kaggle dataset usability ratings on 17000+ public datasets

Here: https://www.kaggle.com/datasets

#ArtificialIntelligence #DeepLearning #MachineLearning

Yann lecun : Great interview with Peter Shor.


https://blogs.scientificamerican.com/cross-check/quantum-computing-for-english-majors/

Self-Supervised Learning

Tutorial by Andrew Zisserman: https://project.inria.fr/paiss/files/2018/07/zisserman-self-supervised.pdf

#CVPR #DeepLearning #SelfSupervisedLearning

Language as an Abstraction for Hierarchical Deep Reinforcement Learning

Jiang et al.: https://arxiv.org/abs/1906.07343

#reinforcementlearning #language #machinelearning

The researchers have constructed a ghostwriter program which utilizes a [Siamese neural network](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf). This process can distinguish the writing styles of two texts. Over time the network is trained using voluminous amounts of data to learn from representations of writing styles (in this case, 130,000 essays were examined from 10,000 students). These are the compared by the program. Siamese neural networks are also being used for recognizing handwritten checks, automatic detection of faces in camera images, and matching queries with indexed documents.

Read more: [https://www.digitaljournal.com/tech-and-science/technology/ai-can-now-catch-90-percent-of-essay-paper-cheats/article/551126#ixzz5qTZyK73D](https://www.digitaljournal.com/tech-and-science/technology/ai-can-now-catch-90-percent-of-essay-paper-cheats/article/551126#ixzz5qTZyK73D)

EasyGen, a visual programming language for text data pipelines for neural nets.
By Mark Riedl.
Colab: https://drive.google.com/open?id=1XNiOuNtMnItl5CPGvRjEvj9C78nDuvXj
Github: https://github.com/markriedl/easygen
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Neurobiologists train artificial neural networks to map the brain
https://bit.do/eVNef

#cellularmorpoholopyneuralnetworks #unsupervisedlearning
#analyzinglargedatasets #CNN #AI

The human brain consists of about 86 billion nerve cells and about as many glial cells. In addition, there are about 100 trillion connections between the nerve cells alone. While mapping all the connections of a human brain remains out of reach, scientists have started to address the problem on a smaller scale. Through the development of serial block-face scanning electron microscopy, all cells and connections of a particular brain area can now be automatically surveyed and displayed in a three-dimensional image.

“It can take several months to survey a 0.3 mm3 piece of brain under an electron microscope. Depending on the size of the brain, this seems like a lot of time for a tiny piece. But even this contains thousands of cells. Such a data set would also require almost 100 terabytes of storage space. However, it is not the collection and storage but rather the data analysis that is the difficult part."

Machine Learning Crash Course with TensorFlow APIs by Google
Free Course

LInk: https://developers.google.com/machine-learning/crash-course/

Build Realistic Human Speech Animations with the New VOCA Model and 4D Face Dataset
https://medium.com/syncedreview/build-realistic-human-speech-animations-with-the-new-voca-model-and-4d-face-dataset-d26ebca37bb7

ICYMI: Facebook researchers open sourced PyRobot, a lightweight, high-level interface that lets AI researchers get up and running with robotics experiments in just hours. No specialized robotics expertise needed!

https://www.profillic.com/paper/arxiv:1906.08236

Distributed Deep Learning Pipelines with PySpark and Keras
https://towardsdatascience.com/distributed-deep-learning-pipelines-with-pyspark-and-keras-a3a1c22b9239

The Functional Neural Process
Louizos et al.: https://arxiv.org/abs/1906.08324
#ArtificialIntelligence #Bayesian #MachineLearning

A scientist at Google Brain devised a way for a machine-learning system to teach itself about how the world works. His name is Ian Goodfellow, and he was one of our 35 Innovators Under 35 in 2017. This year's list comes out on June 25. Stay tuned for the 35 inventors, entrepreneurs, visionaries, humanitarians, and pioneers who will shape tomorrow's technology.
https://www.technologyreview.com/lists/innovators-under-35/2017/inventor/ian-goodfellow/

Unsupervised State Representation Learning in Atari

Learn representations by maximizing mutual information across spatiotemporal features of observations:

https://arxiv.org/abs/1906.08226

resnext101_32x8d_wsl: the ConvNet pre-trained on Instagram hashtags and fine-tuned on ImageNet, yielding a record-breaking 85.4% top-1 accuracy is now available.

https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/

High precision coding in mouse visual cortex

Stringer et al.: https://www.biorxiv.org/content/biorxiv/early/2019/06/21/679324.full.pdf

#brain #cortex #neurons

An early version of fastai for Swift for Tensorflow

"Would you like to train xresnet using 1cycle training on Imagenette, using fastai and the data blocks API?

IN SWIFT?!?

Now you can." - Jeremy Howard

GitHub: https://github.com/fastai/harebrain

#swift #tensorflow #naturallanguage #machinelearning

CS294-158 Deep Unsupervised Learning Spring 2019

Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas - https://sites.google.com/view/berkeley-cs294-158-sp19/home

#unsupervisedlearning #machinelearning #deeplearning

Find The Most Updated and Free Resources of Artificial Intelligence, Machine Learning, Data Science, Deep Learning, Mathematics, Python, and R Programming.



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