Crack the top 40 machine learning interview questions
https://levelup.gitconnected.com/crack-the-top-40-machine-learning-interview-questions-a7526335bcdc
https://levelup.gitconnected.com/crack-the-top-40-machine-learning-interview-questions-a7526335bcdc
Medium
Crack the top 40 machine learning interview questions
Today, take a deep dive into the top 40 machine learning interview questions for any FAANG company.
Contrastive Learning with Hard Negative Samples
Github: https://github.com/joshr17/HCL
Paper: https://arxiv.org/pdf/2010.04592.pdf
Github: https://github.com/joshr17/HCL
Paper: https://arxiv.org/pdf/2010.04592.pdf
Containerized end-to-end analytics of Spotify data using Python
https://pythonawesome.com/containerized-end-to-end-analytics-of-spotify-data-using-python/
https://pythonawesome.com/containerized-end-to-end-analytics-of-spotify-data-using-python/
Announcing the NVIDIA NVTabular Open Beta with Multi-GPU Support and New Data Loaders
https://developer.nvidia.com/blog/announcing-the-nvtabular-open-beta-with-multi-gpu-support-and-new-data-loaders/
https://developer.nvidia.com/blog/announcing-the-nvtabular-open-beta-with-multi-gpu-support-and-new-data-loaders/
Forwarded from TensorFlow
Rethinking Attention with Performers
https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html
@tensorflowblog
https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html
@tensorflowblog
research.google
Rethinking Attention with Performers
Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research Transformer models have achieved state-of-the-art results ac...
FaceShifter — Unofficial PyTorch Implementation
Github: https://github.com/mindslab-ai/faceshifter
Paper: https://arxiv.org/abs/1912.13457
Github: https://github.com/mindslab-ai/faceshifter
Paper: https://arxiv.org/abs/1912.13457
Multilingual T5 (mT5) is a massively multilingual pretrained text-to-text transformer model
Github: https://github.com/google-research/multilingual-t5
Paper: https://arxiv.org/abs/2010.11934v1
Github: https://github.com/google-research/multilingual-t5
Paper: https://arxiv.org/abs/2010.11934v1
GitHub
GitHub - google-research/multilingual-t5
Contribute to google-research/multilingual-t5 development by creating an account on GitHub.
Contrastive learning of general purpose audio representations
https://github.com/google-research/google-research/tree/master/cola
https://github.com/google-research/google-research/tree/master/cola
Bitcoin Trading is Irrational! An Analysis of the Disposition Effect in Bitcoin.
Github: https://github.com/jschatzmann/CryptoDisposition
Paper: https://arxiv.org/abs/2010.12415v1
Github: https://github.com/jschatzmann/CryptoDisposition
Paper: https://arxiv.org/abs/2010.12415v1
Trajectory-wise Multiple Choice Learning for Generalization in Reinforcement Learning
https://github.com/younggyoseo/trajectory_mcl
https://github.com/younggyoseo/trajectory_mcl
Easy-to-interpret neurons may hinder learning in deep neural networks
https://ai.facebook.com/blog/easy-to-interpret-neurons-may-hinder-learning-in-deep-neural-networks/
https://ai.facebook.com/blog/easy-to-interpret-neurons-may-hinder-learning-in-deep-neural-networks/
Facebook
Easy-to-interpret neurons may hinder learning in deep neural networks
What does an AI model “understand” and why? A long-held belief is there are easy-to-interpret neurons -- or “class selective” neurons. For instance, finding neurons that
Abdominal Organ Segmentation A Solved Problem?
Github: https://github.com/MIC-DKFZ/nnunet
Paper: https://arxiv.org/abs/2010.14808v1
@ArtificialIntelligencedl
Github: https://github.com/MIC-DKFZ/nnunet
Paper: https://arxiv.org/abs/2010.14808v1
@ArtificialIntelligencedl
GitHub
GitHub - MIC-DKFZ/nnUNet
Contribute to MIC-DKFZ/nnUNet development by creating an account on GitHub.
Building Neural Networks with PyTorch in Google Colab
https://www.kdnuggets.com/2020/10/building-neural-networks-pytorch-google-colab.html
@ArtificialIntelligencedl
https://www.kdnuggets.com/2020/10/building-neural-networks-pytorch-google-colab.html
@ArtificialIntelligencedl
Random Forest for Time Series Forecasting
https://machinelearningmastery.com/random-forest-for-time-series-forecasting/
@ArtificialIntelligencedl
https://machinelearningmastery.com/random-forest-for-time-series-forecasting/
@ArtificialIntelligencedl
Experimental design for MRI by greedy policy search
Github: https://github.com/Timsey/pg_mri
Paper: https://arxiv.org/abs/2010.16262v1
@ArtificialIntelligencedl
Github: https://github.com/Timsey/pg_mri
Paper: https://arxiv.org/abs/2010.16262v1
@ArtificialIntelligencedl
Bridging Visual Representations’ Decoder Integrates CV Object Detection Frameworks
https://syncedreview.com/2020/11/02/bridging-visual-representations-decoder-integrates-cv-object-detection-frameworks/
@ArtificialIntelligencedl
https://syncedreview.com/2020/11/02/bridging-visual-representations-decoder-integrates-cv-object-detection-frameworks/
@ArtificialIntelligencedl
Synced | AI Technology & Industry Review
‘Bridging Visual Representations’ Decoder Integrates CV Object Detection Frameworks | Synced
NeurIPS 2020 Institute of Automation CAS and Microsoft Research Asia paper presents an attention-based decoder that integrates CV object representations
Forwarded from TensorFlow
New Coral APIs and tools for AI at the edge
https://blog.tensorflow.org/2020/11/new-coral-apis-and-tools-for-ai-at-edge.html
@tensorflowblog
https://blog.tensorflow.org/2020/11/new-coral-apis-and-tools-for-ai-at-edge.html
@tensorflowblog
blog.tensorflow.org
New Coral APIs and tools for AI at the edge
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.