Python Tutorial : Advanced operations

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-tensorflow-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this video, we will cover a selection of advanced operations. Some will be used frequently in later chapters. Others will help you to gain intuition about complex machine learning routines. We have already covered basic operations in TensorFlow, including add, multiply, matmul, and reduce sum. In this lesson, we will move on to more advanced operations, including gradient, reshape, and random. The gradient operation, which we'll use in conjunction with gradient tape, computes the slope of a function at a point. The reshape operation changes the shape of a tensor. And the random module generates a tensor out of randomly-drawn values. In many machine learning problems, you will need to find an optimum--that is, a minimum or maximum. You may, for instance, want to find the model parameters that minimize the loss function or maximize the objective function. Fortunately, we can do this by using the gradient operation, which tells us the slope of a function at a point. We start this process by passing points to the gradient operation until we find one where the gradient is zero. Next, we check if the gradient is increasing or decreasing at that point. If it is increasing, we have minimum. Otherwise, we have a maximum. The plot shows the function y equals x. Notice that the gradient--that is, the slope at a given point, is constant. If we increase x by 1 unit, y also increases by 1 unit. This is not true if we instead consider the function y equals x squared. When x is less than 0, y decreases when x increases. When x is greater than 0, y increases when x increases. Thus, the gradient is initially negative, but becomes positive for x larger than 0. This means that x equals 0 minimizes y. Let's use TensorFlow to compute the gradient.