Understanding TensorFlow

00:00 In this lesson, you’ll gain a general understanding of TensorFlow, which is one of the most powerful and widely used libraries for machine learning. In the next few minutes, you’ll explore simple code examples to understand the basics of TensorFlow, and you’ll also learn about Keras, a crucial part of the TensorFlow framework and its role in simplifying neural network creation and training.

00:22 Additionally, the lesson will guide you through the TensorFlow ecosystem, highlighting where to find pre-trained models and how to effectively use them in your projects.

00:32 Let’s get started with the general features of TensorFlow. The term is derived from the words tensor and flow. You might remember what tensors are from the previous lesson.

00:43 They’re special containers for numbers. TensorFlow employs these tensors to organize and streamline data, optimizing it for the effective training of machine learning and deep learning models.

00:55 TensorFlow was developed by the Google Brain team and released in 2015.

01:02 Now let’s see the code style of TensorFlow. In this code snippet, you’re getting two NumPy arrays, array1, which is 2, 4, 6, and array2, which is 1, 3, 5.

01:16 Then you are transforming them into tensors by using tf.convert_to_tensor, and then you’re multiplying them by using tf.multiply.

01:27 The result is a three by three array, 2, 4, 6, 6, 12, 18, 10, 20, 30. As you can see, you were able to accomplish this task with a few lines of readable code.