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

Get up to speed with machine learning techniques and create smart solutions for different problems

Key Features

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

Book Description

Gaining expertise in artificial intelligence requires an in-depth understanding of the most popular machine learning algorithms. With this book, you'll be able to explore the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the most effective way possible. From Bayesian models, to the MCMC algorithm, and even 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 use TensorFlow and Keras to build deep learning models with concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll discover TensorFlow1.x's advanced features, such as distributed TensorFlow with TF clusters, and also understand the deployment of production models with TensorFlow Serving. As you progress, the book will guide you on how to implement techniques related to object classification, object detection, and image segmentation.

By the end of this Python book, you'll have gained in-depth knowledge of TensorFlow, along with the skills you need for solving artificial intelligence problems.

This Learning Path includes content from the following Packt books:

• 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

• Get up to speed with how a machine 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 autoencoder) models
• Define and train a model for image and video classification
• Deploy your deep learning models and optimize them for high performance

Who this book is for

This Learning Path is for data scientists, machine learning engineers, and artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve predictions of trained models. Basic knowledge of Python programming and machine learning concepts is required to get the most out of this book.

Table of Contents

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