Train and Deploy Machine Learning Model With Web Interface - PyTorch & Flask
Github: https://github.com/imadelh/ML-web-app
Article: https://imadelhanafi.com/posts/train_deploy_ml_model/
Example: https://ml-app.imadelhanafi.com/
Github: https://github.com/imadelh/ML-web-app
Article: https://imadelhanafi.com/posts/train_deploy_ml_model/
Example: https://ml-app.imadelhanafi.com/
GitHub
GitHub - imadelh/ML-web-app: Train and Deploy Simple Machine Learning Model With Web Interface on Jetson Nano
Train and Deploy Simple Machine Learning Model With Web Interface on Jetson Nano - GitHub - imadelh/ML-web-app: Train and Deploy Simple Machine Learning Model With Web Interface on Jetson Nano
Collaborative Experts
This repo provides code for learning and evaluating joint video-text embeddings for the task of video retrieval
https://github.com/albanie/collaborative-experts
This repo provides code for learning and evaluating joint video-text embeddings for the task of video retrieval
https://github.com/albanie/collaborative-experts
GitHub
GitHub - albanie/collaborative-experts: Video embeddings for retrieval with natural language queries
Video embeddings for retrieval with natural language queries - albanie/collaborative-experts
An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt
https://towardsdatascience.com/an-example-of-hyperparameter-optimization-on-xgboost-lightgbm-and-catboost-using-hyperopt-12bc41a271e
https://towardsdatascience.com/an-example-of-hyperparameter-optimization-on-xgboost-lightgbm-and-catboost-using-hyperopt-12bc41a271e
Medium
An Example of Hyperparameter Optimization on XGBoost, LightGBM and CatBoost using Hyperopt
Bonus: Hyperopt-Sklearn
Facebook AI Memory Layer Boosts Network Capacity by a Billion Parameters
https://syncedreview.com/2019/08/02/facebook-ai-memory-layer-boosts-network-capacity-by-a-billion-parameters/
https://syncedreview.com/2019/08/02/facebook-ai-memory-layer-boosts-network-capacity-by-a-billion-parameters/
Synced
Facebook AI Memory Layer Boosts Network Capacity by a Billion Parameters
Recently, Facebook AI Research (FAIR) researchers introduced a structured memory layer which can be easily integrated into a neural network to greatly expand network capacity and the number of para…
An Interactive, Automated 3D Reconstruction of a Fly Brain
http://ai.googleblog.com/2019/08/an-interactive-automated-3d.html
http://ai.googleblog.com/2019/08/an-interactive-automated-3d.html
Googleblog
An Interactive, Automated 3D Reconstruction of a Fly Brain
Image classification with Convolution Neural Networks (CNN)with Keras
https://medium.com/@manasnarkar/image-classification-with-convolution-neural-networks-cnn-with-keras-dbd71c05ed2a?source=topic_page---------0------------------1
https://medium.com/@manasnarkar/image-classification-with-convolution-neural-networks-cnn-with-keras-dbd71c05ed2a?source=topic_page---------0------------------1
Medium
Image classification with Convolution Neural Networks (CNN)with Keras
Introduction
DELTA - a DEep Language Technology plAtform
DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3.
https://github.com/didi/delta
DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3.
https://github.com/didi/delta
GitHub
GitHub - Delta-ML/delta: DELTA is a deep learning based natural language and speech processing platform.
DELTA is a deep learning based natural language and speech processing platform. - GitHub - Delta-ML/delta: DELTA is a deep learning based natural language and speech processing platform.
Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning
https://arxiv.org/abs/1908.02748
https://arxiv.org/abs/1908.02748
arXiv.org
Deblending and Classifying Astronomical Sources with Mask R-CNN...
We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on...
1st edition of "Interpretable Machine Learning
By Christoph Molnar.
Book: https://christophm.github.io/interpretable-ml-book/
#artificalintelligence #deeplearning #machinelearning
By Christoph Molnar.
Book: https://christophm.github.io/interpretable-ml-book/
#artificalintelligence #deeplearning #machinelearning
christophm.github.io
Interpretable Machine Learning
Sparse Networks from Scratch: Faster Training without Losing Performance
https://arxiv.org/abs/1907.04840
https://arxiv.org/abs/1907.04840
arXiv.org
Sparse Networks from Scratch: Faster Training without Losing Performance
We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance...
A new model for word embeddings that are resilient to misspellings
https://ai.facebook.com/blog/-a-new-model-for-word-embeddings-that-are-resilient-to-misspellings-/
https://github.com/facebookresearch/moe?fbclid=IwAR3pCHx4-8oWTqgYqUnKHxcVWdDzPuOVTL0sTidyDBX9J7UPt2HcWxRG9AA
https://ai.facebook.com/blog/-a-new-model-for-word-embeddings-that-are-resilient-to-misspellings-/
https://github.com/facebookresearch/moe?fbclid=IwAR3pCHx4-8oWTqgYqUnKHxcVWdDzPuOVTL0sTidyDBX9J7UPt2HcWxRG9AA
Facebook
A new model for word embeddings that are resilient to misspellings
Misspelling Oblivious Embeddings (MOE) is a new model for word embeddings that are resilient to misspellings, improving the ability to apply word embeddings to real-world situations, where misspellings are common.