TensorFlow 1.x Deep Learning Cookbook

Introduction

In machine learning, there are three different learning paradigms: supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, also known as learning with a teacher, the network is provided with both the inputs and the respective desired outputs. For example, in the MNIST dataset, each image of the handwritten digit has a label signifying the digit value associated with it.

In reinforcement learning, also known as learning with a critic, the network is not provided with the desired output; instead, the environment provides a feedback in terms of reward or punishment. When its output is correct, the environment rewards the network, and when the output is not correct, the environment punishes ...