Sure! TensorFlow is an open-source software library used for numerical computation and machine learning applications. Here are the basic steps to use TensorFlow:

1. Install TensorFlow: You can install TensorFlow by using pip or conda.

2. Import TensorFlow: Once you have installed TensorFlow, you can import it into your Python code.

3. Create a computation graph: TensorFlow uses a computation graph to define the operations that will be executed during training. You can define your computation graph using TensorFlow’s built-in functions or by creating custom functions.

4. Initialize variables: Before you can execute your computation graph, you need to initialize the variables used in your graph.

5. Train your model: To train your model, you need to define a loss function, which measures how well your model is performing, and an optimization algorithm, which updates the model’s parameters to minimize the loss function.

6. Evaluate your model: Once your model is trained, you can evaluate its performance on a test dataset.

7. Use your model: Once your model is trained and evaluated, you can use it to make predictions on new data.

These are the basic steps involved in using TensorFlow. However, TensorFlow is a powerful and complex library, and there are many more advanced techniques and features that you can use to build complex machine learning models.