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Articles related to "loss"


Opening up about my mother's suicide was life-changing

  • She'd been ill for a year and the note she left revealed that she felt she was being a financial and emotional burden on our family -- distracting my sister and I from our college educations, holding my father back in his new job.
  • Opening up about my mom to other suicide loss survivors and hearing their stories gave me comfort and validation.
  • As it hit me that I'd never even thought of my own sister as a suicide loss survivor, I knew I needed to break our silence and get those in my mom's inner circle to talk.
  • On a YouTube series called "Talking About Suicide Loss With..." I started, I speak with other survivors who have inspired me in going public with their stories.
  • I hope my story and film encourage others suffering in silence to talk, acts as a cautionary tale to those who might one day face suicide loss, and teaches all that the silence is not golden.

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Why is Boosting Fitting Residual

  • Boosting algorithm searches for the optimal weak learner functions that minimize loss function over the training data at every iteration.
  • Many articles describe boosting algorithm as “recursively adding weak learners to fit residual made by previous learners”.
  • Boosting or Forward Stagewise Additive Modeling is an ensemble learning method that combines many weak learners to form a collection that acts like a strong learner.
  • In this expression, fₘ₋₁ represents predicted value from the previous trees.
  • yᵢ - fₘ₋₁ will output residual from the previous learners, rᵢₘ.
  • Thus, to minimize the square losses, every new weak learner will fit the residual.
  • For a 2-class classification problem, AdaBoost, a famous boosting algorithm introduced by Freund and Schapire in 1997, uses exponential loss.
  • At every iteration, training samples will be re-weighted based on prediction errors made by previous trees.
  • Subsequent tree will fit the re-weighted training sample.

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Why is Boosting Fitting Residual

  • Boosting algorithm searches for the optimal weak learner functions that minimize loss function over the training data at every iteration.
  • Many articles describe boosting algorithm as “recursively adding weak learners to fit residual made by previous learners”.
  • Boosting or Forward Stagewise Additive Modeling is an ensemble learning method that combines many weak learners to form a collection that acts like a strong learner.
  • In this expression, fₘ₋₁ represents predicted value from the previous trees.
  • yᵢ - fₘ₋₁ will output residual from the previous learners, rᵢₘ.
  • Thus, to minimize the square losses, every new weak learner will fit the residual.
  • For a 2-class classification problem, AdaBoost, a famous boosting algorithm introduced by Freund and Schapire in 1997, uses exponential loss.
  • At every iteration, training samples will be re-weighted based on prediction errors made by previous trees.
  • Subsequent tree will fit the re-weighted training sample.

save | comments | report | share on


Why is Boosting Fitting Residual

  • Boosting algorithm searches for the optimal weak learner functions that minimize loss function over the training data at every iteration.
  • Many articles describe boosting algorithm as “recursively adding weak learners to fit residual made by previous learners”.
  • Boosting or Forward Stagewise Additive Modeling is an ensemble learning method that combines many weak learners to form a collection that acts like a strong learner.
  • In this expression, fₘ₋₁ represents predicted value from the previous trees.
  • yᵢ - fₘ₋₁ will output residual from the previous learners, rᵢₘ.
  • Thus, to minimize the square losses, every new weak learner will fit the residual.
  • For a 2-class classification problem, AdaBoost, a famous boosting algorithm introduced by Freund and Schapire in 1997, uses exponential loss.
  • At every iteration, training samples will be re-weighted based on prediction errors made by previous trees.
  • Subsequent tree will fit the re-weighted training sample.

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Check out the Air Force's new T-7A Red Hawk, named for the legendary Tuskegee Airmen

  • Acting Secretary of the Air Force Matthew Donovan announced on Monday that the US Air Force would introduce a new training aircraft named for the Tuskegee Airmen, the first African-American squadron in the US Army Air Corps.
  • Donovan was joined onstage by Col. Charles McGee, one of the original Tuskegee Airmen.
  • McGee served in World War II, Korea, and Vietnam, and flew over 400 combat missions, according to a release from the Air Force.
  • The first squadron, the 99th Fighter Squadron, were America's first African-American military pilots.
  • According to Tuskegee University, 1,000 African-American pilots were trained at Tuskegee from 1941 to 1946.
  • Although they were finally allowed to train to be pilots, the Tuskegee Airmen still lived in segregated quarters while training.
  • The Air Force ordered 351 of the new aircraft, as well as 46 simulators and additional ground equipment, to the tune of $9.2 billion.

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Getting Started With Bounding Box Regression In TensorFlow

  • Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me!
  • ). We’ll now learn a basic concept in Object Detection called Bounding Box Regression.
  • We’ll implement a Bounding Box regression model in TensorFlow with Keras API.
  • The dataset contains 373 images from three classes ( cucumbers, eggplant and mushroom ) with their bounding box annotations in XML files.
  • Yes, that’s possible with LabelImg. It’s a great tool for drawing bounding boxes over images and saving them to the PASCAL-VOC format quickly.
  • ). To determine the average IOU score over the test dataset and also to calculate the class accuracy, we use the below code.
  • My first impressions regarding bounding box regression were not could but its something you should definitely if you’re further going to learn YOLO or SSD object detectors.

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Getting Started With Bounding Box Regression In TensorFlow

  • Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me!
  • ). We’ll now learn a basic concept in Object Detection called Bounding Box Regression.
  • We’ll implement a Bounding Box regression model in TensorFlow with Keras API.
  • The dataset contains 373 images from three classes ( cucumbers, eggplant and mushroom ) with their bounding box annotations in XML files.
  • Yes, that’s possible with LabelImg. It’s a great tool for drawing bounding boxes over images and saving them to the PASCAL-VOC format quickly.
  • ). To determine the average IOU score over the test dataset and also to calculate the class accuracy, we use the below code.
  • My first impressions regarding bounding box regression were not could but its something you should definitely if you’re further going to learn YOLO or SSD object detectors.

save | comments | report | share on


Getting Started With Bounding Box Regression In TensorFlow

  • Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me!
  • ). We’ll now learn a basic concept in Object Detection called Bounding Box Regression.
  • We’ll implement a Bounding Box regression model in TensorFlow with Keras API.
  • The dataset contains 373 images from three classes ( cucumbers, eggplant and mushroom ) with their bounding box annotations in XML files.
  • Yes, that’s possible with LabelImg. It’s a great tool for drawing bounding boxes over images and saving them to the PASCAL-VOC format quickly.
  • ). To determine the average IOU score over the test dataset and also to calculate the class accuracy, we use the below code.
  • My first impressions regarding bounding box regression were not could but its something you should definitely if you’re further going to learn YOLO or SSD object detectors.

save | comments | report | share on


Getting Started With Bounding Box Regression In TensorFlow

  • Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me!
  • ). We’ll now learn a basic concept in Object Detection called Bounding Box Regression.
  • We’ll implement a Bounding Box regression model in TensorFlow with Keras API.
  • The dataset contains 373 images from three classes ( cucumbers, eggplant and mushroom ) with their bounding box annotations in XML files.
  • Yes, that’s possible with LabelImg. It’s a great tool for drawing bounding boxes over images and saving them to the PASCAL-VOC format quickly.
  • ). To determine the average IOU score over the test dataset and also to calculate the class accuracy, we use the below code.
  • My first impressions regarding bounding box regression were not could but its something you should definitely if you’re further going to learn YOLO or SSD object detectors.

save | comments | report | share on


Getting Started With Bounding Box Regression In TensorFlow

  • Any ML learner could like to see nice bounding boxes around an object in an image ( at least for me!
  • ). We’ll now learn a basic concept in Object Detection called Bounding Box Regression.
  • We’ll implement a Bounding Box regression model in TensorFlow with Keras API.
  • The dataset contains 373 images from three classes ( cucumbers, eggplant and mushroom ) with their bounding box annotations in XML files.
  • Yes, that’s possible with LabelImg. It’s a great tool for drawing bounding boxes over images and saving them to the PASCAL-VOC format quickly.
  • ). To determine the average IOU score over the test dataset and also to calculate the class accuracy, we use the below code.
  • My first impressions regarding bounding box regression were not could but its something you should definitely if you’re further going to learn YOLO or SSD object detectors.

save | comments | report | share on