#003 D TF Gradient Descent in TensorFlow Master Data Science

In this post we will see how to implement Gradient Descent using TensorFlow. Next, we will define our variable \(\omega \) and we will initialize it with \(-3 \). With the following peace of code we will also define our cost function \(J(\omega) (\omega – 3) 2 \). With the next two lines of code, we specify the initialization of our variables (here we have just one variable \(\omega \) and the gradient descent for minimizing our cost function with the learning rate of \(0.01 \). Then we will define a session as sess and we will run the init so we will initialize the variable \(\omega \).