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Python: Advanced Guide to Artificial Intelligence
Python: Advanced Guide to Artificial Intelligence

Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python

By Dr. Giuseppe Bonaccorso , Rajalingappaa Shanmugamani
$15.99 per month
Book Dec 2018 pages 1st Edition

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Key benefits

  • Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation
  • Build deep learning models for object detection, image classification, similarity learning, and more
  • Build, deploy, and scale end-to-end deep neural network models in a production environment

Description

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

What you will learn

Explore how an ML model can be trained, optimized, and evaluated Work with Autoencoders and Generative Adversarial Networks Explore the most important Reinforcement Learning techniques Build end-to-end deep learning (CNN, RNN, and Autoencoders) models

Product Details

Country selected

Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789957211
Category :

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Product Details


Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789957211
Category :

Table of Contents

31 Chapters
Title Page Chevron down icon Chevron up icon
About Packt Chevron down icon Chevron up icon
Contributors Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Machine Learning Model Fundamentals Chevron down icon Chevron up icon
2. Introduction to Semi-Supervised Learning Chevron down icon Chevron up icon
3. Graph-Based Semi-Supervised Learning Chevron down icon Chevron up icon
4. Bayesian Networks and Hidden Markov Models Chevron down icon Chevron up icon
5. EM Algorithm and Applications Chevron down icon Chevron up icon
6. Hebbian Learning and Self-Organizing Maps Chevron down icon Chevron up icon
7. Clustering Algorithms Chevron down icon Chevron up icon
8. Advanced Neural Models Chevron down icon Chevron up icon
9. Classical Machine Learning with TensorFlow Chevron down icon Chevron up icon
10. Neural Networks and MLP with TensorFlow and Keras Chevron down icon Chevron up icon
11. RNN with TensorFlow and Keras Chevron down icon Chevron up icon
12. CNN with TensorFlow and Keras Chevron down icon Chevron up icon
13. Autoencoder with TensorFlow and Keras Chevron down icon Chevron up icon
14. TensorFlow Models in Production with TF Serving Chevron down icon Chevron up icon
15. Deep Reinforcement Learning Chevron down icon Chevron up icon
16. Generative Adversarial Networks Chevron down icon Chevron up icon
17. Distributed Models with TensorFlow Clusters Chevron down icon Chevron up icon
18. Debugging TensorFlow Models Chevron down icon Chevron up icon
19. Tensor Processing Units Chevron down icon Chevron up icon
20. Getting Started Chevron down icon Chevron up icon
21. Image Classification Chevron down icon Chevron up icon
22. Image Retrieval Chevron down icon Chevron up icon
23. Object Detection Chevron down icon Chevron up icon
24. Semantic Segmentation Chevron down icon Chevron up icon
25. Similarity Learning Chevron down icon Chevron up icon
1. Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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