​Using AI to detect COVID-19 misinformation and exploitative content

🔗 Using AI to detect COVID-19 misinformation and exploitative content
We’re sharing details about how Facebook is using AI to help detect COVID-19 misinformation and respond to other integrity challenges related to the pandemic.

​Using AI to detect COVID-19 misinformation and exploitative content

🔗 Using AI to detect COVID-19 misinformation and exploitative content
We’re sharing details about how Facebook is using AI to help detect COVID-19 misinformation and respond to other integrity challenges related to the pandemic.

​Using AI to detect COVID-19 misinformation and exploitative content

🔗 Using AI to detect COVID-19 misinformation and exploitative content
We’re sharing details about how Facebook is using AI to help detect COVID-19 misinformation and respond to other integrity challenges related to the pandemic.

​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...

​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...

​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...

​Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery

🔗 Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery
Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique,

​Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery

🔗 Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery
Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique,

​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз ⏳ 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch

​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз ⏳ 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch

​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз ⏳ 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch

​Deep Demand Forecasting with Amazon SageMaker

🔗 Deep Demand Forecasting with Amazon SageMaker
Use Deep Learning for Demand Forecasting

​Deep Demand Forecasting with Amazon SageMaker

🔗 Deep Demand Forecasting with Amazon SageMaker
Use Deep Learning for Demand Forecasting

​Deep Demand Forecasting with Amazon SageMaker

🔗 Deep Demand Forecasting with Amazon SageMaker
Use Deep Learning for Demand Forecasting

​Detecting Weird Data: Conformal Anomaly Detection

🔗 Detecting Weird Data: Conformal Anomaly Detection
Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of…

​Detecting Weird Data: Conformal Anomaly Detection

🔗 Detecting Weird Data: Conformal Anomaly Detection
Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of…

​Detecting Weird Data: Conformal Anomaly Detection

🔗 Detecting Weird Data: Conformal Anomaly Detection
Weird data is important. Often in data science, the goal is to discover trends in the data. However, consider doctors looking at images of…

​Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning

In this tutorial, you will learn how to fine-tune ResNet using Keras, TensorFlow, and Deep Learning.

https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/

Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to fine-tune ResNet using Keras, TensorFlow, and Deep Learning.

​COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning - PyImageSearch

🔗 COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning.

​COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning - PyImageSearch

🔗 COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning.