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

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
Machine Learning Model Fundamentals
Introduction to Semi-Supervised Learning
Graph-Based Semi-Supervised Learning
Bayesian Networks and Hidden Markov Models
EM Algorithm and Applications
Hebbian Learning and Self-Organizing Maps
Clustering Algorithms
Advanced Neural Models
Classical Machine Learning with TensorFlow
Neural Networks and MLP with TensorFlow and Keras
RNN with TensorFlow and Keras
CNN with TensorFlow and Keras
Autoencoder with TensorFlow and Keras
TensorFlow Models in Production with TF Serving
Deep Reinforcement Learning
Generative Adversarial Networks
Distributed Models with TensorFlow Clusters
Debugging TensorFlow Models
Tensor Processing Units
Getting Started
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning