Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units

Chapter 13. Autoencoder with TensorFlow and Keras

Autoencoder is a neural network architecture that is often associated with unsupervised learning, dimensionality reduction, and data compression. Autoencoders learn to produce the same output as given to the input layer by using lesser number of neurons in the hidden layers. This allows hidden layers to learn the features of input with lesser number of parameters. This process of using lesser number of neurons to learn the features of the input data, in turn, reduces the dimensionality of the input dataset.

An autoencoder architecture has two stages: encoder and decoder. In the encoder stage, the model learns to represent the input to a compressed vector with lesser dimensions, and in the decoder stage, the model learns to represent the compressed vector to an output vector. The loss is calculated as entropy distance between the output and input, thus by minimizing the loss, we learn parameters that encode the input into a representation that...