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    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

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    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial IntelligenceGet up to speed with machine learning techniques and create smart solutions for dif
    SKU: GBPAGTAI

    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    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.

    Get immediately download Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    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

    GETTING READY TO DOWNLOAD: Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence
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    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    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.

    Get immediately download Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence

    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

    GETTING READY TO DOWNLOAD: Giuseppe Bonaccorso - Python - Advanced Guide to Artificial Intelligence