Building Regression Models Using TensorFlow

This course is a part of Building Machine Learning Solutions with Tensorflow, a 11-course Paths series from Pluralsight.

TensorFlow is all about building neural networks that can "learn" functions, and linear regression can be learnt by the simplest possible neural network - of just 1 neuron! In contrast, the XOR function requires 3 neurons arranged in 2 layers, and smart image recognition can require thousands of neurons. In this course, Building Regression Models using TensorFlow, you'll learn how the neurons in neural networks learn non-linear functions. First, you'll begin by learning functions such as XOR, and how to train different gradient descent optimizers. Next, you'll dive into the implications of choosing activation functions, such as softmax and ReLU. Finally, you'll explore the use of built-in estimators in Tensorflow. By the end of this course, you'll have a better understanding of how neurons "learn", and how neural networks in TensorFlow are set up and trained to execute operations such as regression and classification.