Python: Advanced Guide to Artificial Intelligence: Expert machine learning syste

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Python: Advanced Guide to Artificial Intelligence by Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

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 ...

FORMAT
Paperback LANGUAGE
English CONDITION
Brand New Publisher Description

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problemsKey Features

• 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

Book DescriptionThis 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 problemsThis 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

Who this book is forThis Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Table of Contents

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

Long Description

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features 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 Book 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 Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Details ISBN1789957214 Author Rajalingappaa Shanmugamani Publisher Packt Publishing Limited Year 2018 ISBN-10 1789957214 ISBN-13 9781789957211 Pages 764 Publication Date 2018-12-21 Short Title Python: Advanced Guide to Artificial Intelligence Language English Format Paperback UK Release Date 2018-12-21 Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2018-12-21 NZ Release Date 2018-12-21 Subtitle Expert machine learning systems and intelligent agents using Python DEWEY 006.3 Audience Professional & Vocational Country of Origin US Product Class Description Computing: Professional & Programming
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