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

OpenAI Gym 101


OpenAI Gym is a Python-based toolkit for the research and development of reinforcement learning algorithms. OpenAI Gym provides more than 700 opensource contributed environments at the time of writing. With OpenAI, you can also create your own environments. The biggest advantage is that OpenAI provides a unified interface for working with these environments, and takes care of running the simulation while you focus on the reinforcement learning algorithms.

Note

The research paper describing OpenAI Gym is available at this link: http://arxiv.org/abs/1606.01540

You can install OpenAI Gym using the following command:

pip3 install gym

Note

If the above command does not work, then you can find further help with installation at the following link: https://github.com/openai/gym#installation

  1. Let us print the number of available environments in OpenAI Gym:

Note

You can follow along with the code in the Jupyter notebook ch-13a_Reinforcement_Learning_NN in the code bundle of this book.

all_env...