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-le... Exercise: Go at the end of the above notebook to see the exercise Do you want to learn technology from me? Check https://resources.codebasics.io/8CcOdp for my affordable video courses. 🔖 Hashtags 🔖 #lossfunction #costfunction #costfunctionneuralnetwork #lossfunctionneuralnetwork #costfunctiondeeplearning #lossfunctiondeeplearning Why not MSE for logistic regression: https://towardsdatascience.com/why-no... Next video:    • Gradient Descent For Neural Network | Deep...   Previous video:    • Matrix Basics | Deep Learning Tutorial 10 ...   Deep learning playlist:    • Deep Learning With Tensorflow 2.0, Keras a...   Machine learning playlist : https://www.youtube.com/playlist?list... Prerequisites for this series:    1: Python tutorials (first 16 videos): https://www.youtube.com/playlist?list...   2: Pandas tutorials(first 8 videos):    • Pandas Tutorial (Data Analysis In Python)   3: Machine learning playlist (first 16 videos): https://www.youtube.com/playlist?list... Website: https://resources.codebasics.io/8CcOdp Facebook:   / codebasicshub   Twitter:   / codebasicshub