Introduction to Convolutional Neural Networks
The article focuses on explaining key components in CNN and its implementation using Keras python library.
https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html
The article focuses on explaining key components in CNN and its implementation using Keras python library.
https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html
DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
Recursive Feature Pyramid implements thinking twice at the macro level, where the outputs of FPN are brought back to each stage of the bottom-up backbone through feedback connections
Github: https://github.com/joe-siyuan-qiao/DetectoRS
Paper: https://arxiv.org/abs/2006.02334v1
Recursive Feature Pyramid implements thinking twice at the macro level, where the outputs of FPN are brought back to each stage of the bottom-up backbone through feedback connections
Github: https://github.com/joe-siyuan-qiao/DetectoRS
Paper: https://arxiv.org/abs/2006.02334v1
A Scalable and Cloud-Native Hyperparameter Tuning System
Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.
Github: https://github.com/kubeflow/katib
Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/
Paper: https://arxiv.org/abs/2006.02085v1
Katib is a Kubernetes-based system for Hyperparameter Tuning and Neural Architecture Search. Katib supports a number of ML frameworks, including TensorFlow, Apache MXNet, PyTorch, XGBoost, and others.
Github: https://github.com/kubeflow/katib
Getting started with Katib: https://www.kubeflow.org/docs/components/hyperparameter-tuning/hyperparameter/
Paper: https://arxiv.org/abs/2006.02085v1
Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine
Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.
It currently supports TensorFlow, PyTorch, TorchScript, and Keras.
https://eng.uber.com/introducing-neuropod/
Github: https://github.com/uber/neuropod
Neuropod Tutorial: https://neuropod.ai/tutorial/
Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.
It currently supports TensorFlow, PyTorch, TorchScript, and Keras.
https://eng.uber.com/introducing-neuropod/
Github: https://github.com/uber/neuropod
Neuropod Tutorial: https://neuropod.ai/tutorial/
Uber Blog
Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine | Uber Blog
Developed by Uber ATG, Neuropod is an abstraction layer that provides a universal interface to run models across any deep learning framework.
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Part 2: Fast, scalable and accurate NLP: Why TFX is a perfect match for deploying BERT
https://blog.tensorflow.org/2020/06/part-2-fast-scalable-and-accurate-nlp.html
Code: https://colab.research.google.com/github/tensorflow/workshops/blob/master/blog/TFX_Pipeline_for_Bert_Preprocessing.ipynb
https://blog.tensorflow.org/2020/06/part-2-fast-scalable-and-accurate-nlp.html
Code: https://colab.research.google.com/github/tensorflow/workshops/blob/master/blog/TFX_Pipeline_for_Bert_Preprocessing.ipynb
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PEGASUS: A State-of-the-Art Model for Abstractive Text Summarization
https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html
Github: https://github.com/google-research/pegasus
Paper: https://arxiv.org/abs/1912.08777
https://ai.googleblog.com/2020/06/pegasus-state-of-art-model-for.html
Github: https://github.com/google-research/pegasus
Paper: https://arxiv.org/abs/1912.08777
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
Github: https://github.com/implus/GFocal
Paper: https://arxiv.org/abs/2006.04388v1
Github: https://github.com/implus/GFocal
Paper: https://arxiv.org/abs/2006.04388v1
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
Realtime object detection is improving quickly. The rate of improvement is improving even more quickly. The results are stunning.
https://blog.roboflow.ai/yolov5-is-here/
Github: https://github.com/ultralytics/yolov5
GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart
Realtime object detection is improving quickly. The rate of improvement is improving even more quickly. The results are stunning.
https://blog.roboflow.ai/yolov5-is-here/
Github: https://github.com/ultralytics/yolov5
GCP Quickstart: https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart
Roboflow Blog
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
Less than 50 days after the release YOLOv4, YOLOv5 improves accessibility for realtime object detection.
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
June 29, YOLOv5 has released the first official version of the repository. We wrote a new deep dive on YOLOv5.
June 12, 8:08 AM CDT Update: In response…
Deploy a Machine Learning Pipeline to the Cloud Using a Docker Container
https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html
https://www.kdnuggets.com/2020/06/deploy-machine-learning-pipeline-cloud-docker.html
VirTex: Learning Visual Representations from Textual Annotations
VirTex is a pretraining approach which uses semantically dense captions to learn visual representations.VirTex matches or outperforms models which use ImageNet for pretraining -- both supervised or unsupervised -- despite using up to 10x fewer images.
https://kdexd.github.io/virtex/
Github: https://github.com/kdexd/virtex
Paper: arxiv.org/abs/2006.06666
VirTex is a pretraining approach which uses semantically dense captions to learn visual representations.VirTex matches or outperforms models which use ImageNet for pretraining -- both supervised or unsupervised -- despite using up to 10x fewer images.
https://kdexd.github.io/virtex/
Github: https://github.com/kdexd/virtex
Paper: arxiv.org/abs/2006.06666
Synthesizing High-Resolution Images with StyleGAN2
https://www.youtube.com/watch?v=9QuDh3W3lOY&feature=emb_logo
Article: https://news.developer.nvidia.com/synthesizing-high-resolution-images-with-stylegan2/
https://www.youtube.com/watch?v=9QuDh3W3lOY&feature=emb_logo
Article: https://news.developer.nvidia.com/synthesizing-high-resolution-images-with-stylegan2/
YouTube
Synthesizing High-Resolution Images with StyleGAN2
This new project called StyleGAN2, developed by NVIDIA Research, and presented at CVPR 2020, uses transfer learning to produce seemingly infinite numbers of portraits in an infinite variety of painting styles. The work builds on the team’s previously published…
Rethinking the Truly Unsupervised Image-to-Image Translation - Official PyTorch Implementation
Here proposed the truly unsupervised image-to-image translation method (TUNIT) that simultaneously learns to separate image domains via an information-theoretic approach and generate corresponding images using the estimated domain labels.
Github: https://github.com/clovaai/tunit
Paper: https://arxiv.org/abs/2006.06500v1
Here proposed the truly unsupervised image-to-image translation method (TUNIT) that simultaneously learns to separate image domains via an information-theoretic approach and generate corresponding images using the estimated domain labels.
Github: https://github.com/clovaai/tunit
Paper: https://arxiv.org/abs/2006.06500v1
Using Multi-Scale Attention for Semantic Segmentation
https://devblogs.nvidia.com/using-multi-scale-attention-for-semantic-segmentation/
Paper: https://arxiv.org/abs/2005.10821
https://devblogs.nvidia.com/using-multi-scale-attention-for-semantic-segmentation/
Paper: https://arxiv.org/abs/2005.10821
NVIDIA Technical Blog
Using Multi-Scale Attention for Semantic Segmentation
There’s an important technology that is commonly used in autonomous driving, medical imaging, and even Zoom virtual backgrounds: semantic segmentation. That’s the process of labelling pixels in an…
From singing to musical scores: Estimating pitch with SPICE and Tensorflow Hub
Pitch is quantified by frequency, measured in Hertz (Hz), where one Hz corresponds to one cycle per second. The higher the frequency, the higher the note.
https://blog.tensorflow.org/2020/06/estimating-pitch-with-spice-and-tensorflow-hub.html
Model: https://tfhub.dev/google/spice/2
Colab code: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/spice.ipynb
Pitch is quantified by frequency, measured in Hertz (Hz), where one Hz corresponds to one cycle per second. The higher the frequency, the higher the note.
https://blog.tensorflow.org/2020/06/estimating-pitch-with-spice-and-tensorflow-hub.html
Model: https://tfhub.dev/google/spice/2
Colab code: https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/spice.ipynb
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SimCLR - A Simple Framework for Contrastive Learning of Visual Representations
The findings described in this paper can potentially be harnessed to improve accuracy in any application of computer vision where it is more expensive or difficult to label additional data than to train larger models.
Github: https://github.com/google-research/simclr
Paper: https://arxiv.org/abs/2006.10029
The findings described in this paper can potentially be harnessed to improve accuracy in any application of computer vision where it is more expensive or difficult to label additional data than to train larger models.
Github: https://github.com/google-research/simclr
Paper: https://arxiv.org/abs/2006.10029
Data-Efficient GANs with DiffAugment
Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples.
Github: https://github.com/mit-han-lab/data-efficient-gans
Paper: https://arxiv.org/abs/2006.10738
Training code: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples.
Github: https://github.com/mit-han-lab/data-efficient-gans
Paper: https://arxiv.org/abs/2006.10738
Training code: https://github.com/mit-han-lab/data-efficient-gans/tree/master/DiffAugment-stylegan2
The Most Important Fundamentals of PyTorch you Should Know
https://blog.exxactcorp.com/the-most-important-fundamentals-of-pytorch-you-should-know/
Code: https://github.com/tirthajyoti/PyTorch_Machine_Learning
https://blog.exxactcorp.com/the-most-important-fundamentals-of-pytorch-you-should-know/
Code: https://github.com/tirthajyoti/PyTorch_Machine_Learning
Exxactcorp
Blog - the most important fundamentals of pytorch you should know | Exxact
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Machine Learning in Dask
In this article you can learn how Dask works with a huge dataset on local machine or in a distributed manner.
https://www.kdnuggets.com/2020/06/machine-learning-dask.html
In this article you can learn how Dask works with a huge dataset on local machine or in a distributed manner.
https://www.kdnuggets.com/2020/06/machine-learning-dask.html