Building Regression Models Using TensorFlow
Pluralsight
Course Summary
TensorFlow is the tool of choice for building deep learning applications. In this course, you'll learn how the neurons in neural networks learn non-linear functions, and how neural networks execute operations such as regression and classification.
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Course Description
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.
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Course Syllabus
Course Overview- 1m 35s
—Course Overview 1m 35sLearning Using Neurons- 45m 56s
—Understanding Deep Learning 5m 3s
—Deep Learning as a Representation Learning System 5m 14s
—Neurons as Learning Units 4m 25s
—Understanding a Neuron 7m 38s
—Activation Functions 2m 23s
—Regression: The Simplest Neural Network 4m 58s
—XOR: A Slightly More Complex Neural Network 7m 27s
—Learning XOR 5m 1s
—Choice of Activation Function 2m 20s
—Prequisites and Course Outline 1m 23sBuilding Linear Regression Models Using TensorFlow- 46m 43sBuilding Logistic Regression Models Using TensorFlow- 43m 28sBuilding Generalized Linear Models Using Estimators- 21m 51s
