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)

Exercise: Go at the end of the above notebook to see the exercise

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#lossfunction #costfunction #costfunctionneuralnetwork #lossfunctionneuralnetwork #costfunctiondeeplearning #lossfunctiondeeplearning

Why not MSE for logistic regression:

Prerequisites for this series:   

[ashwin]

Loving this series. You're so good at looking at everything from the learners perspective and clarifying everything! Much better than all the other tutorials I've tried

[viniciushedler]

When changing y_predicted to avoid errors with log(0) it is possible to do [ abs( y - epsilon ) for y in y_predicted ], which will work for both y=1 and y=0 cases

[priyabratapanda]

Sir in binary cross entropy the loss function should be c=-- 1/n sum (y*log(p) + (1-y)log(1-p))
Where p is the probability of the sample for the positive class.
By the way nice intuitive lectures, I love you way of teaching 🙏

[BahauddinAziz]

Feedback: You can create chapter of the tutorials, will be helpful to navigate

[meenakshichippa]

U should not hesitate to mention Andrew NG 's name. We all learn from somebody. Andrew NG has his own expertise and u have ur own.
It is always perfectly okay to share from sources we learn.

[sakalagamingyt]

Instead of doing manually we can avoid log(0) using numpy:
epsilon = 1e-15
y_predicted_new = np.clip(y_predicted, epsilon, 1-epsilon)

[mohitupadhayay]

That log loss vs MSE article was a BOOM!

[GamerBoy-iijc]

it's very easy exercise i just replace the absolute with square and it done...thanks for this playlist

[harshkartiksingh]

i was frightened from that corona virus mate, it freaked me out😂😂, thanks for this awesome video.

[izharkhankhattak]

Great man does a great job. Thank you so much.

[maheshsingh]

Am just stuck with the series.. It's super interesting

[useryaya-rh]

We need to upgrade to read the full article that you are referring to in the video...

[bilkisuismail]

I was wondering why we have 0.5 as a result for MAE with Python code and 0.26 with Numpy code

[karanveersingh]

min(i, 1 - epsilon) puts all values below even if it is above 1

[abhishekfnu]

Thank you so much for this course, sir

[pickase]

for real i really performed all those code without using your notebook sir because of the virus warning.😅😅

[manujarora]

at 17:32 the formula needs a parentheses to begin before yi and end at the end of expression.

[asamadawais]

You are excellent boss. Allah bless you!

[leelavathigarigipati]

Thank you so for the detailed explanation. Could you please explain about SGD, ADAM optimizations.

[ronyjoseph]

very benificial videos, can u upload videos of some online or offline job oppurtunities of machine, deep learning