Loss or Cost Function | Deep Learning Tutorial 11 (Tensorflow Tutorial, Keras Python)

Loss or a cost function is an important concept we need to understand if you want to grasp how a neural network trains itself. We will go over various loss functions in this video such as mean absolute error (a.k.a MAE), mean squared error (a.k.a MSE), log loss or binary cross entropy. After going through theory we will implement these loss functions in python. It is important to go through this implementation as it might be useful during your interviews (if you are targeting a role of a data scientist or a machine learning engineer)

Code: https://github.com/codebasics/....deep-learning-keras-
Exercise: Go at the end of the above notebook to see the exercise

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Why not MSE for logistic regression:
https://towardsdatascience.com..../why-not-mse-as-a-lo

Next video: https://www.youtube.com/watch?v=pXGBHV3y8rs&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=12

Previous video: https://www.youtube.com/watch?v=Wibxjrxf5ko&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=10

Deep learning playlist: https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
Machine learning playlist : https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

Prerequisites for this series:   
1: Python tutorials (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u  
2: Pandas tutorials(first 8 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u
3: Machine learning playlist (first 16 videos): https://www.youtube.com/playli....st?list=PLeo1K3hjS3u

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