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
Contributors
Preface
19
Tensor Processing Units
Index

Autoencoder types


Autoencoder architectures can be found in a variety of configurations such as simple autoencoders, sparse autoencoders, denoising autoencoders, and convolutional autoencoders.

  • Simple autoencoder: In simple autoencoder, the hidden layers have lesser number of nodes or neurons as compared to the input. For example, in the MNIST dataset, an input of 784 features can be connected to the hidden layer of 512 nodes or 256 nodes, which is connected to the 784-feature output layer. Thus, during training, the 784 features would be learned by only 256 nodes. Simple autoencoders are also known as undercomplete autoencoders.

    Simple autoencoder could be single-layer or multi-layer. Generally, single-layer autoencoder does not perform very good in production. Multi-layer autoencoder has more than one hidden layer, divided into encoder and decoder groupings. Encoder layers encode a large number of features into a smaller number of neurons, and decoder layers then decode the learned compressed...