Advanced NLP with spaCy by Ines Montani, core developers of spaCy: https://course.spacy.io

In this course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Creating accurate #MachineLearning Models which are capable of identifying and localizing multiple objects in a single image remained a core challenge in computer vision. But, with recent advancements in #DeepLearning, #ObjectDetection applications are easier to develop than ever before. So, if you want to know how to perform Real-Time Object Detection using #Tensorflow, you can refer to the following article:
https://medium.com/edureka/tensorflow-object-detection-tutorial-8d6942e73adc

TensorWatch: New debugging and visualization tool designed for deep learning, from Microsoft Research!

Github: https://github.com/microsoft/tensorwatch

“Instead of labeling images, a researcher now simply plays video games all day long.” 🤔

Free supervision from video games
https://bit.do/eTw8d

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan and Quoc V. Le: https://arxiv.org/abs/1905.11946
Github: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
Blog: https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html?m=1
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Defending Against Neural Fake News
Zellers et al.: https://arxiv.org/abs/1905.12616
#ArtificialIntelligence #DeepLearning #Society

Very proud and enthusiastic to contribute to this project!


https://www.korbit.ai/machinelearning

Multi-Sample Dropout for Accelerated Training and Better Generalization
Hiroshi Inoue: https://arxiv.org/abs/1905.09788
#ArtificialIntelligence #NeuralComputing #MachineLearning

Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall and Max Welling: https://arxiv.org/abs/1905.11697
#ArtificialIntelligence #DeepLearning #MachineLearning

SinGAN: Learning a Generative Model from a Single Natural Image
Shaham et al.: https://arxiv.org/abs/1905.01164v1
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

VAE-SBD
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation

Geoffrey Hinton explains neural networks at Google i/o 2019

https://m.youtube.com/watch?feature=youtu.be&v=lsDf_S1oYOg

Google AI #GeoffreyHinton

How Intelligence Is Transforming The Aviation Industry
https://medium.com/@morejitu/how-artificial-intelligence-is-transforming-the-aviation-industry-c49a1deeddba

#ArtificialIntelligence #AI #aviation #startup #technology

TorchVision 0.3 is out!: segmentation models, detection models, new datasets and more.

Tutorial on customizing/retraining Mask-RCNN for instance segmentation on Collab: https://colab.research.google.com/github/pytorch/vision/blob/temp-tutorial/tutorials/torchvision_finetuning_instance_segmentation.ipynb

https://pytorch.org/blog/torchvision03/

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
https://arxiv.org/abs/1807.01281
#artificialintelligence #deeplearning #reinforcementlearning

CoqGym
A Learning Environment for Theorem Proving with the Coq proof assistant
By Princeton Vision & Learning Lab: https://github.com/princeton-vl/CoqGym
#Logic #ComputerScience #ArtificialIntelligence #MachineLearning

This paper introduces a new method to play SuperMario Bros. using RL agent and without knowing the scores from the environment (pure exploration). They employ optical flow for evaluating the novelty of states to guide the RL agent.

https://www.profillic.com/paper/arxiv:1905.10071

In 2017, Google announced a Tensor Processing Unit (#TPU) — a custom application-specific integrated circuit (ASIC) built specifically for #machinelearning. A year later, TPUs were moved to the cloud and made open for commercial use.

Following the line of CPUs and GPUs, Tensor Processing Units (TPUs) are Google’s custom-developed application-specific integrated circuits (ASICs) that are supposed to accelerate machine learning workloads. They are designed specifically for Google’s #TensorFlow framework, a symbolic math library that is used for #neuralnetworks.

https://medium.com/sciforce/understanding-tensor-processing-units-10ff41f50e78

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Fedorov et al.: https://arxiv.org/pdf/1905.12107.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning

These Insect-Inspired Robots Don't Need GPS For Orientation
https://www.humanbrainproject.eu/en/follow-hbp/news/these-insect-inspired-robots-don-t-need-gps-for-orientation