Building Regression Models Using TensorFlow 1
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.
What you'll learn
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.
Table of contents
- Version Check 0m
- Understanding Deep Learning 5m
- Deep Learning as a Representation Learning System 5m
- Neurons as Learning Units 4m
- Understanding a Neuron 8m
- Activation Functions 2m
- Regression: The Simplest Neural Network 5m
- XOR: A Slightly More Complex Neural Network 7m
- Learning XOR 5m
- Choice of Activation Function 2m
- Prequisites and Course Outline 1m