TTNet: Real-time temporal and spatial video analysis of table tennis
Voeikov et al.: https://arxiv.org/abs/2004.09927
#ArtificialIntelligence #DeepLearning #MachineLearning

Should data scientists learn JavaScript?

https://www.google.com/amp/s/www.freecodecamp.org/news/should-data-scientists-learn-javascript-e611d45804b8/amp/

Deep learning with graph-structured representations
T.N. Kipf : https://dare.uva.nl/search?identifier=1b63b965-24c4-4bcd-aabb-b849056fa76d
#DeepLearning #Graph #NeuralNetworks

400+ textbooks free to download
CS books on Python, deep learning, data science & AI.
Springer: https://bit.ly/SpringerCS
#DeepLearning #Python #Programming #Coding

ML Code Completeness Checklist
https://medium.com/paperswithcode/ml-code-completeness-checklist-e9127b168501

Data Science resume samples:

https://resumegenius.com/resume-samples/data-scientist-resume-example

https://www.kickresume.com/en/help-center/data-scientist-resume-sample/

https://www.velvetjobs.com/resume/data-scientist-intern-resume-sample

Every month, members of this group will look at a new dataset and work individually to analyze it and apply machine learning concepts. Then, we will all provide constructive feedback to each other.

https://forms.gle/URGuBQ1ALEHij1mq9

@ArtificialIntelligenceArticles

JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Mao et al.: https://arxiv.org/abs/2005.03361
#ArtificialIntelligence #DeepLearning #NLP

PyRetri: An Open-Source Deep Learning Based Unsupervised Image Retrieval Library Built on PyTorch
Paper: https://arxiv.org/abs/2005.02154
Github: https://github.com/PyRetri/PyRetri

Object Detection: EfficientDet (SOTA), MobileNetv3 and YOLO using OpenCV and TensorFlow.

- Blog post: https://imadelhanafi.com/posts/object_detection_yolo_efficientdet_mobilenet/

- Github repo: https://github.com/imadelh/Object-Detection_MobileNetv3-EfficientDet-YOLO

- Live version: https://vision.imadelhanafi.com/predict/v1?model=MODEL_NAME&image_url=URL (example: https://vision.imadelhanafi.com/predict/v1?model=mobilenet&image_url=https://imadelhanafi.com/data/draft/random/img4.jpg)

AI postdocs available! Stanford AI Lab is delighted to offer postdocs to some exciting young AI researchers in these difficult times. Positions for 2 years working with SAIL faculty. If you’ve procrastinated, this is the week to get your application in!
https://ai.stanford.edu/postdoctoral-applications/


@ArtificialIntelligenceArticles

From Ian Goodfellow and other Google researchers: A novel approach to generating high-resolution images, guided by small inputs, that results in perceptually convincing details (called Latent Adversarial Generator (LAG))

For project and code or API request: https://www.catalyzex.com/paper/arxiv:2003.02365

Yann LeCun thinks tensor networks are similar to convolutional neural networks

https://www.preposterousuniverse.com/blog/2015/05/05/does-spacetime-emerge-from-quantum-information/

A Metric Learning Reality Check
Musgrave et al.: https://arxiv.org/abs/2003.08505
"Our results show that when hyperparameters are properly tuned via cross-validation, most methods perform similarly to one another"
#ArtificialIntelligence #DeepLearning #MachineLearning

Text classification with Transformer
Apoorv Nandan, Colab : https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_with_transformer.ipynb
#ArtificialIntelligence #DeepLearning #Transformer

NVIDIA’s New Ampere Data Center GPU
NVIDIA A100 GPU is a 20x AI performance leap and an end-to-end machine learning accelerator : https://nvidianews.nvidia.com/news/nvidias-new-ampere-data-center-gpu-in-full-production
#NVIDIA #GPU #DeepLearning

Using Reinforcement Learning in the Algorithmic Trading Problem
Ponomarev et al.: https://arxiv.org/abs/2002.11523
#DeepLearning #ReinforcementLearning #Trading

LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery

For project and dataset: https://www.catalyzex.com/paper/arxiv:2005.02264

They collected images of 216.27 sq. km lands across Poland, a country in Central Europe, 39.51 sq. km with resolution 50 cm per pixel and 176.76 sq. km with resolution 25 cm per pixel and manually fine annotated three following classes of objects: buildings, woodlands, and water.