- published: 15 Jun 2020
- views: 74229
Loss function
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (sometimes called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized.
In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this is usually economic cost or regret. In classification, it is the penalty for an incorrect classification of an example. In actuarial science, it is used in an insurance context to model benefits paid over premiums, particularly since the works of Harald Cramér in the 1920s. In optimal control the loss is the penalty for failing to achieve a desired value. In financial risk management the function is precisely mapped to a monetary loss.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Loss_function
Podcasts:
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133 - What are Loss functions in machine learning?
published: 15 Jun 2020 -
Loss Functions - EXPLAINED!
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work! SUBSCRIBE FOR MORE CONTENT! RESEOURCES [1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077 [2] CVPR paper presentation: https://www.youtube.com/watch?v=BmNKbnF69eY [3] Regression Loss Functions: https://alexisalulema.com/2017/12/07/loss-functions-part-1/ [4] Classification Losses: https://alexisalulema.com/2017/12/07/loss-functions-part-1/ [5] ML cheat sheet for loss functions: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html [6] 7 loss functions with python code: https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/ [7] A Blog for most common Loss Functions: https://towardsdatascience....
published: 20 Jan 2020 -
Loss functions in Neural Networks - EXPLAINED!
Let's talk about Loss Functions in neural networks ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ RESOURCES [1] Code to build first neural network: https://github.com/ajhalthor/deep-learning-101/tree/main PLAYLISTS FROM MY CHANNEL ⭕ Reinforcement Learning 101: https://youtube.com/playlist?list=PLTl9hO2Oobd9kS--NgVz0EPNyEmygV1Ha&si=AuThDZJwG19cgTA8 Natural Language Processing 101: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE&si=LsVy8RDPu8jeO-cc ⭕ Transformers from Scratch: https://youtube.com/playlist?list=PLTl9hO2Oobd_bzXUpzKMKA3liq2kj6LfE ⭕ ChatGPT Playlist: https://youtube.com/play...
published: 29 Jan 2024 -
Loss Functions : Data Science Basics
What are loss functions in the context of machine learning?
published: 16 Nov 2020 -
Intuitively Understanding the Cross Entropy Loss
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. The video will draw the connections between the KL divergence and the cross entropy loss, and touch on some practical considerations. Twitter: https://twitter.com/AdianLiusie
published: 05 Jul 2021 -
Session On Different Types Of Loss Function In Deep Learning
Join Ineuron Full Stack Data Science Course with Placement Guarantee https://ineuron1.viewpage.co/FullStackDatascienceJGP ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=krishnaik&utm_content=description-only All Playlist In My channel Interview Playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVM0zN0cgJrfT6TK2ypCpQdY Complete DL Playlist: https://www.youtube.com/watch?v=9jA0KjS7V_c&list=PLZoTAELRMXVPGU70ZGsckrMdr0FteeRUi Julia Playlist: https://www.youtube.com/watch?v=Bxp1YFA6M...
published: 16 Feb 2021 -
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision. Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3...
published: 11 Aug 2017 -
Loss Functions in Deep Learning | Deep Learning | CampusX
In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how these functions impact model performance. ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or q...
published: 23 Mar 2022 -
BEST TEAS to drink FOR OVERALL HEALTH || 6 Best Teas for Health Benefits
Discover the incredible health benefits of various teas in our latest video! Learn how Green Tea aids in weight loss and cognitive function, how Hibiscus Tea manages hypertension and cholesterol, and how Black Tea improves blood vessel function and reduces inflammation. Dive into the anti-diabetic properties of Cinnamon Tea and the anti-inflammatory power of Turmeric Tea for joint health. Finally, find out how Chamomile Tea promotes better sleep and reduces anxiety. Incorporate these teas into your daily routine for a healthier lifestyle. Don't forget to like and share this video, and let your friends join you on the journey to a healthier you! Green Tea Super Antioxidant https://amzn.to/4bkM0se Hibiscus Tea Bags https://amzn.to/4eBpCO5 Black Tea, Regenerative Organic https://amzn....
published: 23 Jun 2024 -
Loss Functions Explained
Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems. Code for this video: https://github.com/llSourcell/loss_functions_explained Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval instagram: https://www.instagram.com/sirajr...
published: 24 Jul 2018
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6:50
133 - What are Loss functions in machine learning?
- Order: Reorder
- Duration: 6:50
- Uploaded Date: 15 Jun 2020
- views: 74229
8:30
Loss Functions - EXPLAINED!
- Order: Reorder
- Duration: 8:30
- Uploaded Date: 20 Jan 2020
- views: 128504
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work!
SUBSCRIBE FOR MORE CONTENT!
RESEOURCES
[1]...
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work!
SUBSCRIBE FOR MORE CONTENT!
RESEOURCES
[1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077
[2] CVPR paper presentation: https://www.youtube.com/watch?v=BmNKbnF69eY
[3] Regression Loss Functions: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[4] Classification Losses: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[5] ML cheat sheet for loss functions: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
[6] 7 loss functions with python code: https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/
[7] A Blog for most common Loss Functions: https://towardsdatascience.com/understanding-the-3-most-common-loss-functions-for-machine-learning-regression-23e0ef3e14d3
[8] Modeling the Huber loss: https://www.textbook.ds100.org/ch/10/modeling_abs_huber.html
[9] Notes on Subgradients: https://see.stanford.edu/materials/lsocoee364b/01-subgradients_notes.pdf
[10] Code to get up to speed: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html
[11] What is the difference between KL divergence and Cross Entropy loss: https://stats.stackexchange.com/questions/357963/what-is-the-difference-cross-entropy-and-kl-divergence
[12] A great video explanation of Entropy, Cross Entropy and KL divergence: https://www.youtube.com/watch?v=ErfnhcEV1O8
https://wn.com/Loss_Functions_Explained
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work!
SUBSCRIBE FOR MORE CONTENT!
RESEOURCES
[1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077
[2] CVPR paper presentation: https://www.youtube.com/watch?v=BmNKbnF69eY
[3] Regression Loss Functions: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[4] Classification Losses: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[5] ML cheat sheet for loss functions: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
[6] 7 loss functions with python code: https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/
[7] A Blog for most common Loss Functions: https://towardsdatascience.com/understanding-the-3-most-common-loss-functions-for-machine-learning-regression-23e0ef3e14d3
[8] Modeling the Huber loss: https://www.textbook.ds100.org/ch/10/modeling_abs_huber.html
[9] Notes on Subgradients: https://see.stanford.edu/materials/lsocoee364b/01-subgradients_notes.pdf
[10] Code to get up to speed: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html
[11] What is the difference between KL divergence and Cross Entropy loss: https://stats.stackexchange.com/questions/357963/what-is-the-difference-cross-entropy-and-kl-divergence
[12] A great video explanation of Entropy, Cross Entropy and KL divergence: https://www.youtube.com/watch?v=ErfnhcEV1O8
- published: 20 Jan 2020
- views: 128504
8:14
Loss functions in Neural Networks - EXPLAINED!
- Order: Reorder
- Duration: 8:14
- Uploaded Date: 29 Jan 2024
- views: 6131
Let's talk about Loss Functions in neural networks
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Let's talk about Loss Functions in neural networks
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https://wn.com/Loss_Functions_In_Neural_Networks_Explained
Let's talk about Loss Functions in neural networks
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- published: 29 Jan 2024
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16:40
Loss Functions : Data Science Basics
- Order: Reorder
- Duration: 16:40
- Uploaded Date: 16 Nov 2020
- views: 31431
What are loss functions in the context of machine learning?
What are loss functions in the context of machine learning?
https://wn.com/Loss_Functions_Data_Science_Basics
What are loss functions in the context of machine learning?
- published: 16 Nov 2020
- views: 31431
5:24
Intuitively Understanding the Cross Entropy Loss
- Order: Reorder
- Duration: 5:24
- Uploaded Date: 05 Jul 2021
- views: 72899
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. The video will...
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. The video will draw the connections between the KL divergence and the cross entropy loss, and touch on some practical considerations.
Twitter: https://twitter.com/AdianLiusie
https://wn.com/Intuitively_Understanding_The_Cross_Entropy_Loss
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. The video will draw the connections between the KL divergence and the cross entropy loss, and touch on some practical considerations.
Twitter: https://twitter.com/AdianLiusie
- published: 05 Jul 2021
- views: 72899
1:42:32
Session On Different Types Of Loss Function In Deep Learning
- Order: Reorder
- Duration: 1:42:32
- Uploaded Date: 16 Feb 2021
- views: 74453
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- published: 16 Feb 2021
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1:14:40
Lecture 3 | Loss Functions and Optimization
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- Duration: 1:14:40
- Uploaded Date: 11 Aug 2017
- views: 869074
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and d...
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.
Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent
Slides:
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf
--------------------------------------------------------------------------------------
Convolutional Neural Networks for Visual Recognition
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Website:
http://cs231n.stanford.edu/
For additional learning opportunities please visit:
http://online.stanford.edu/
https://wn.com/Lecture_3_|_Loss_Functions_And_Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.
Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent
Slides:
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf
--------------------------------------------------------------------------------------
Convolutional Neural Networks for Visual Recognition
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Website:
http://cs231n.stanford.edu/
For additional learning opportunities please visit:
http://online.stanford.edu/
- published: 11 Aug 2017
- views: 869074
59:56
Loss Functions in Deep Learning | Deep Learning | CampusX
- Order: Reorder
- Duration: 59:56
- Uploaded Date: 23 Mar 2022
- views: 59676
In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how...
In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how these functions impact model performance.
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⌚Time Stamps⌚
00:00 - Intro
01:09 - What is loss function?
11:08 - Loss functions in deep learning
14:20 - Loss function vs cost function
24:35 - Advantages/Disadvantages
59:13 - Outro
https://wn.com/Loss_Functions_In_Deep_Learning_|_Deep_Learning_|_Campusx
In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how these functions impact model performance.
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✨ Hashtags✨
#DeepLearning #LossFunctions #NeuralNetworks #MachineLearning #AI #LearningBasics #SimplifiedLearning #modeltraining
⌚Time Stamps⌚
00:00 - Intro
01:09 - What is loss function?
11:08 - Loss functions in deep learning
14:20 - Loss function vs cost function
24:35 - Advantages/Disadvantages
59:13 - Outro
- published: 23 Mar 2022
- views: 59676
5:49
BEST TEAS to drink FOR OVERALL HEALTH || 6 Best Teas for Health Benefits
- Order: Reorder
- Duration: 5:49
- Uploaded Date: 23 Jun 2024
- views: 9
Discover the incredible health benefits of various teas in our latest video! Learn how Green Tea aids in weight loss and cognitive function, how Hibiscus Tea ma...
Discover the incredible health benefits of various teas in our latest video! Learn how Green Tea aids in weight loss and cognitive function, how Hibiscus Tea manages hypertension and cholesterol, and how Black Tea improves blood vessel function and reduces inflammation. Dive into the anti-diabetic properties of Cinnamon Tea and the anti-inflammatory power of Turmeric Tea for joint health. Finally, find out how Chamomile Tea promotes better sleep and reduces anxiety. Incorporate these teas into your daily routine for a healthier lifestyle. Don't forget to like and share this video, and let your friends join you on the journey to a healthier you!
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The information on the Vitality Bits channel is meant for learning purposes only, not as medical or professional advice. It's important to talk to your doctor before starting any new diet or treatment, or if you have questions about your health. If you think you have a medical problem, see your healthcare provider right away.
This video and its description include affiliate links from Amazon and other affiliate programs. By clicking on these product links, I may earn a small commission.
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00:00 Top 6 Teas for a Healthier You!
00:11 The Metabolism Booster
02:05 The Blood Pressure Manager
03:16 The Cholesterol Buster
04:29 The Blood Sugar Stabilizer
04:52 The Joint Soother
05:15 The Sleep Enhancer
05:37 Cheers to a Healthier You!
https://wn.com/Best_Teas_To_Drink_For_Overall_Health_||_6_Best_Teas_For_Health_Benefits
Discover the incredible health benefits of various teas in our latest video! Learn how Green Tea aids in weight loss and cognitive function, how Hibiscus Tea manages hypertension and cholesterol, and how Black Tea improves blood vessel function and reduces inflammation. Dive into the anti-diabetic properties of Cinnamon Tea and the anti-inflammatory power of Turmeric Tea for joint health. Finally, find out how Chamomile Tea promotes better sleep and reduces anxiety. Incorporate these teas into your daily routine for a healthier lifestyle. Don't forget to like and share this video, and let your friends join you on the journey to a healthier you!
Green Tea Super Antioxidant https://amzn.to/4bkM0se
Hibiscus Tea Bags https://amzn.to/4eBpCO5
Black Tea, Regenerative Organic https://amzn.to/45GodC6
Cinnamon Horchata Stress & Sleep https://amzn.to/4cAJGOL
Honey Chai Turmeric Vitality Tea https://amzn.to/4cf7bNC
Comforting Chamomile Tea https://amzn.to/3znbNTg
Disclaimer
The information on the Vitality Bits channel is meant for learning purposes only, not as medical or professional advice. It's important to talk to your doctor before starting any new diet or treatment, or if you have questions about your health. If you think you have a medical problem, see your healthcare provider right away.
This video and its description include affiliate links from Amazon and other affiliate programs. By clicking on these product links, I may earn a small commission.
#health #longlife #healthyliving #healthtips #TeaBenefits #GreenTea #HibiscusTea #BlackTea #CinnamonTea #TurmericTea #chamomiletea #herbaltea
OUTLINE:
00:00 Top 6 Teas for a Healthier You!
00:11 The Metabolism Booster
02:05 The Blood Pressure Manager
03:16 The Cholesterol Buster
04:29 The Blood Sugar Stabilizer
04:52 The Joint Soother
05:15 The Sleep Enhancer
05:37 Cheers to a Healthier You!
- published: 23 Jun 2024
- views: 9
12:56
Loss Functions Explained
- Order: Reorder
- Duration: 12:56
- Uploaded Date: 24 Jul 2018
- views: 113518
Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem ...
Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.
Code for this video:
https://github.com/llSourcell/loss_functions_explained
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More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/
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https://wn.com/Loss_Functions_Explained
Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.
Code for this video:
https://github.com/llSourcell/loss_functions_explained
Please Subscribe! And like. And comment. That's what keeps me going.
Want more education? Connect with me here:
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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey
More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/
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- published: 24 Jul 2018
- views: 113518
6:50
133 - What are Loss functions in machine learning?
published: 15 Jun 2020
133 - What are Loss functions in machine learning?
133 - What are Loss functions in machine learning?
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- published: 15 Jun 2020
- views: 74229
8:30
Loss Functions - EXPLAINED!
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher ...
published: 20 Jan 2020
Loss Functions - EXPLAINED!
Loss Functions - EXPLAINED!
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- published: 20 Jan 2020
- views: 128504
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work!
SUBSCRIBE FOR MORE CONTENT!
RESEOURCES
[1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077
[2] CVPR paper presentation: https://www.youtube.com/watch?v=BmNKbnF69eY
[3] Regression Loss Functions: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[4] Classification Losses: https://alexisalulema.com/2017/12/07/loss-functions-part-1/
[5] ML cheat sheet for loss functions: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
[6] 7 loss functions with python code: https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code/
[7] A Blog for most common Loss Functions: https://towardsdatascience.com/understanding-the-3-most-common-loss-functions-for-machine-learning-regression-23e0ef3e14d3
[8] Modeling the Huber loss: https://www.textbook.ds100.org/ch/10/modeling_abs_huber.html
[9] Notes on Subgradients: https://see.stanford.edu/materials/lsocoee364b/01-subgradients_notes.pdf
[10] Code to get up to speed: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html
[11] What is the difference between KL divergence and Cross Entropy loss: https://stats.stackexchange.com/questions/357963/what-is-the-difference-cross-entropy-and-kl-divergence
[12] A great video explanation of Entropy, Cross Entropy and KL divergence: https://www.youtube.com/watch?v=ErfnhcEV1O8
8:14
Loss functions in Neural Networks - EXPLAINED!
Let's talk about Loss Functions in neural networks
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published: 29 Jan 2024
Loss functions in Neural Networks - EXPLAINED!
Loss functions in Neural Networks - EXPLAINED!
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- published: 29 Jan 2024
- views: 6131
Let's talk about Loss Functions in neural networks
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16:40
Loss Functions : Data Science Basics
What are loss functions in the context of machine learning?
published: 16 Nov 2020
Loss Functions : Data Science Basics
Loss Functions : Data Science Basics
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- published: 16 Nov 2020
- views: 31431
What are loss functions in the context of machine learning?
5:24
Intuitively Understanding the Cross Entropy Loss
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of th...
published: 05 Jul 2021
Intuitively Understanding the Cross Entropy Loss
Intuitively Understanding the Cross Entropy Loss
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- published: 05 Jul 2021
- views: 72899
This video discusses the Cross Entropy Loss and provides an intuitive interpretation of the loss function through a simple classification set up. The video will draw the connections between the KL divergence and the cross entropy loss, and touch on some practical considerations.
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1:42:32
Session On Different Types Of Loss Function In Deep Learning
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published: 16 Feb 2021
Session On Different Types Of Loss Function In Deep Learning
Session On Different Types Of Loss Function In Deep Learning
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1:14:40
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss ...
published: 11 Aug 2017
Lecture 3 | Loss Functions and Optimization
Lecture 3 | Loss Functions and Optimization
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- published: 11 Aug 2017
- views: 869074
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.
Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent
Slides:
http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf
--------------------------------------------------------------------------------------
Convolutional Neural Networks for Visual Recognition
Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
Website:
http://cs231n.stanford.edu/
For additional learning opportunities please visit:
http://online.stanford.edu/
59:56
Loss Functions in Deep Learning | Deep Learning | CampusX
In this video, we'll understand the concept of Loss Functions and their role in training n...
published: 23 Mar 2022
Loss Functions in Deep Learning | Deep Learning | CampusX
Loss Functions in Deep Learning | Deep Learning | CampusX
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- published: 23 Mar 2022
- views: 59676
In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how these functions impact model performance.
============================
Do you want to learn from me?
Check my affordable mentorship program at : https://learnwith.campusx.in
============================
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⌚Time Stamps⌚
00:00 - Intro
01:09 - What is loss function?
11:08 - Loss functions in deep learning
14:20 - Loss function vs cost function
24:35 - Advantages/Disadvantages
59:13 - Outro
5:49
BEST TEAS to drink FOR OVERALL HEALTH || 6 Best Teas for Health Benefits
Discover the incredible health benefits of various teas in our latest video! Learn how Gre...
published: 23 Jun 2024
BEST TEAS to drink FOR OVERALL HEALTH || 6 Best Teas for Health Benefits
BEST TEAS to drink FOR OVERALL HEALTH || 6 Best Teas for Health Benefits
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- published: 23 Jun 2024
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Discover the incredible health benefits of various teas in our latest video! Learn how Green Tea aids in weight loss and cognitive function, how Hibiscus Tea manages hypertension and cholesterol, and how Black Tea improves blood vessel function and reduces inflammation. Dive into the anti-diabetic properties of Cinnamon Tea and the anti-inflammatory power of Turmeric Tea for joint health. Finally, find out how Chamomile Tea promotes better sleep and reduces anxiety. Incorporate these teas into your daily routine for a healthier lifestyle. Don't forget to like and share this video, and let your friends join you on the journey to a healthier you!
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OUTLINE:
00:00 Top 6 Teas for a Healthier You!
00:11 The Metabolism Booster
02:05 The Blood Pressure Manager
03:16 The Cholesterol Buster
04:29 The Blood Sugar Stabilizer
04:52 The Joint Soother
05:15 The Sleep Enhancer
05:37 Cheers to a Healthier You!
12:56
Loss Functions Explained
Which loss function should you use to train your machine learning model? The huber loss? C...
published: 24 Jul 2018
Loss Functions Explained
Loss Functions Explained
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- published: 24 Jul 2018
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Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.
Code for this video:
https://github.com/llSourcell/loss_functions_explained
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More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/
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Loss function
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (sometimes called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized.
In statistics, typically a loss function is used for parameter estimation, and the event in question is some function of the difference between estimated and true values for an instance of data. The concept, as old as Laplace, was reintroduced in statistics by Abraham Wald in the middle of the 20th century. In the context of economics, for example, this is usually economic cost or regret. In classification, it is the penalty for an incorrect classification of an example. In actuarial science, it is used in an insurance context to model benefits paid over premiums, particularly since the works of Harald Cramér in the 1920s. In optimal control the loss is the penalty for failing to achieve a desired value. In financial risk management the function is precisely mapped to a monetary loss.
This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Loss_function
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