How vFlat used the TFLite GPU delegate for real time inference to scan books
https://medium.com/tensorflow/how-vflat-used-the-tflite-gpu-delegate-for-real-time-inference-to-scan-books-df3bb86a4eb7
https://medium.com/tensorflow/how-vflat-used-the-tflite-gpu-delegate-for-real-time-inference-to-scan-books-df3bb86a4eb7
Medium
How vFlat used the TFLite GPU delegate for real time inference to scan books
A guest post by Kunwoo Park, Moogung Kim, Eunsung Han
Awesome Machine Learning
A curated list of awesome machine learning frameworks, libraries and software (by language).
libraries: https://github.com/josephmisiti/awesome-machine-learning
books: https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
A curated list of awesome machine learning frameworks, libraries and software (by language).
libraries: https://github.com/josephmisiti/awesome-machine-learning
books: https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md
GitHub
GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.
A curated list of awesome Machine Learning frameworks, libraries and software. - josephmisiti/awesome-machine-learning
Synthetic Celebrity Faces at 128x128 Resolution After Tuning Generated by the Progressive Growing GAN
How to Train a Progressive Growing GAN in Keras for Synthesizing Faces
https://machinelearningmastery.com/how-to-train-a-progressive-growing-gan-in-keras-for-synthesizing-faces/
How to Train a Progressive Growing GAN in Keras for Synthesizing Faces
https://machinelearningmastery.com/how-to-train-a-progressive-growing-gan-in-keras-for-synthesizing-faces/
MachineLearningMastery.com
How to Train a Progressive Growing GAN in Keras for Synthesizing Faces - MachineLearningMastery.com
Generative adversarial networks, or GANs, are effective at generating high-quality synthetic images. A limitation of GANs is that the are only capable of generating relatively small images, such as 64x64 pixels. The Progressive Growing GAN is an extension…
Joint Speech Recognition and Speaker Diarization via Sequence Transduction
http://ai.googleblog.com/2019/08/joint-speech-recognition-and-speaker.html
http://ai.googleblog.com/2019/08/joint-speech-recognition-and-speaker.html
Googleblog
Joint Speech Recognition and Speaker Diarization via Sequence Transduction
Forwarded from Artificial Intelligence
ai ,machine learning
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code
https://paperswithcode.com/sota
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code
https://paperswithcode.com/sota
GitHub
Papers with code
Papers with code has 13 repositories available. Follow their code on GitHub.
Music Transformer: Generating Music with Long-Term Structure
Code: https://github.com/jason9693/MusicTransformer-tensorflow2.0
Article: https://arxiv.org/abs/1809.04281
Code: https://github.com/jason9693/MusicTransformer-tensorflow2.0
Article: https://arxiv.org/abs/1809.04281
GitHub
GitHub - jason9693/MusicTransformer-tensorflow2.0: implementation of music transformer with tensorflow-2.0 (ICLR2019)
implementation of music transformer with tensorflow-2.0 (ICLR2019) - jason9693/MusicTransformer-tensorflow2.0
👍1
Using The Super-Resolution Convolutional Neural Network for Image Restoration
https://medium.com/datadriveninvestor/using-the-super-resolution-convolutional-neural-network-for-image-restoration-ff1e8420d846
https://medium.com/datadriveninvestor/using-the-super-resolution-convolutional-neural-network-for-image-restoration-ff1e8420d846
Medium
Using The Super Resolution Convolutional Neural Network for Image Restoration
Welcome to this tutorial on single-image super-resolution. The goal of super-resolution (SR) is to recover a high resolution image from a…
A Gentle Introduction to StyleGAN the Style Generative Adversarial Network
https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/
https://machinelearningmastery.com/introduction-to-style-generative-adversarial-network-stylegan/
On-Device, Real-Time Hand Tracking with MediaPipe
http://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html
http://ai.googleblog.com/2019/08/on-device-real-time-hand-tracking-with.html
research.google
On-Device, Real-Time Hand Tracking with MediaPipe
Posted by Valentin Bazarevsky and Fan Zhang, Research Engineers, Google Research The ability to perceive the shape and motion of hands can be a v...
🔥New models in 17 and 100 languages XLM/mBERT pytorch
LM supports multi-GPU and multi-node training
https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models
LM supports multi-GPU and multi-node training
https://github.com/facebookresearch/XLM#pretrained-cross-lingual-language-models
GitHub
GitHub - facebookresearch/XLM: PyTorch original implementation of Cross-lingual Language Model Pretraining.
PyTorch original implementation of Cross-lingual Language Model Pretraining. - facebookresearch/XLM
Turbo, An Improved Rainbow Colormap for Visualization
http://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
http://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
research.google
Turbo, An Improved Rainbow Colormap for Visualization
Posted by Anton Mikhailov, Senior Software Engineer, Daydream False color maps show up in many applications in computer vision and machine learni...
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
https://github.com/uber/ludwig
https://github.com/uber/ludwig
GitHub
GitHub - ludwig-ai/ludwig: Low-code framework for building custom LLMs, neural networks, and other AI models
Low-code framework for building custom LLMs, neural networks, and other AI models - ludwig-ai/ludwig
Data Visualization Curriculum
A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair.
https://github.com/uwdata/visualization-curriculum
A data visualization curriculum of interactive notebooks, using Vega-Lite and Altair.
https://github.com/uwdata/visualization-curriculum
GitHub
GitHub - uwdata/visualization-curriculum: A data visualization curriculum of interactive notebooks.
A data visualization curriculum of interactive notebooks. - uwdata/visualization-curriculum
Deep Learning Illustrated: Building Natural Language Processing Models
https://blog.dominodatalab.com/deep-learning-illustrated-building-natural-language-processing-models/
https://blog.dominodatalab.com/deep-learning-illustrated-building-natural-language-processing-models/
domino.ai
Building Natural Language Processing Models with Keras
Deep Learning Illustrated: Building Natural Language Processing Models
TensorFlow with Apache Arrow Datasets
Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning.
https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f
Also TensorFlow 2.0 Release Candidate:
https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-rc0
Apache Arrow enables the means for high-performance data exchange with TensorFlow that is both standardized and optimized for analytics and machine learning.
https://medium.com/tensorflow/tensorflow-with-apache-arrow-datasets-cdbcfe80a59f
Also TensorFlow 2.0 Release Candidate:
https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-rc0
Medium
TensorFlow with Apache Arrow Datasets
An Overview of Apache Arrow Datasets Plus Example To Run Keras Model Training
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
http://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html
http://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html
blog.research.google
Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
Exploring Weight Agnostic Neural Networks
article: http://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html
habr: https://habr.com/ru/post/465369/
article: http://ai.googleblog.com/2019/08/exploring-weight-agnostic-neural.html
habr: https://habr.com/ru/post/465369/
blog.research.google
Exploring Weight Agnostic Neural Networks
This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch.
https://github.com/dsgiitr/d2l-pytorch
https://github.com/dsgiitr/d2l-pytorch
GitHub
GitHub - dsgiitr/d2l-pytorch: This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from…
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch. - dsgiitr/d2l-pytorch