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

Recurrent networks

All the models that we have analyzed until now have a common feature. Once the training process is completed, the weights are frozen and the output depends only on the input sample. Clearly, this is the expected behavior of a classifier, but there are many scenarios where a prediction must take into account the history of the input values. A time series is a classic example. Let's suppose that we need to predict the temperature for the next week. If we try to use only the last known x(t) value and an MLP trained to predict x(t+1), it's impossible to take into account temporal conditions like the season, the history of the season over the years, the position in the season, and so on. The regressor will be able to associate the output that yields the minimum average error, but in real-life situations, this isn't enough. The only reasonable way to solve this problem is to define a new architecture for the artificial neuron, to provide it with a memory. This concept is shown...