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

RNN variants

The RNN architecture has been extended in many ways to accommodate the extra needs in certain problems and to overcome the shortcomings of simple RNN models. We list some of the major extensions to the RNN architecture below.

  • Bidirectional RNN (BRNN) is used when the output depends on both the previous and future elements of a sequence. BRNN is implemented by stacking two RNNs, known as forward and backward Layer, and the output is the result of the hidden state of both the RNNs. In the forward layer, the memory state h flows from time step t to time step t+1 and in the backward layer the memory state flows from time step t to time step t-1. Both the layers take same input xt at time step t, but they jointly produce the output at time step t.
  • Deep Bidirectional RNN (DBRNN) extends the BRNN further by adding multiple layers. The BRNN has hidden layers or cells across the time dimensions. However, by stacking BRNN, we get the hierarchical presentation in DBRNN. One of the significant...