After the initial installation and setup, we can start building deep learning models by simply loading the TensorFlow library into the R environment:

1. Let's start by simulating some dummy data:

x_data = matrix(runif(1000*2),nrow = 1000,ncol = 1)
y_data = matrix(runif(1000),nrow = 1000,ncol = 1)

2. Now, we need to initialize some TensorFlow variables; that is, the weights and biases:

W <- tf$Variable(tf$random_uniform(shape(1L), -1.0, 1.0))
b <- tf$Variable(tf$zeros(shape(1L)))

3. Now, let's define the model:

y_hat <- W * x_data + b

4. Then, we need to define the loss function and optimizer:

loss <- tf$reduce_mean((y_hat - y_data) ^ 2)
optimizer <- tf$train$GradientDescentOptimizer(0.5)
train <- optimizer$minimize(loss)

5. Next, we launch the computation graph and initialize the TensorFlow variables:

sess = tf$Session()
sess$run(tf$global_variables_initializer())

6. We train the model to fit the training data:

for (step in 1:201) {
 sess$run(train)
 if (step %% 20 == 0)
 cat(step, "-", sess$run(W), sess$run(b), "
")
}

Finally, we close the session:

sess$close()

Here are the results of every 20th iteration: