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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

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Profile Icon Giuseppe Bonaccorso Profile Icon Rajalingappaa Shanmugamani
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Course Dec 2018 764 pages 1st Edition
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Profile Icon Giuseppe Bonaccorso Profile Icon Rajalingappaa Shanmugamani
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Course Dec 2018 764 pages 1st Edition

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

Who is this book 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.

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

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 21, 2018
Length: 764 pages
Edition : 1st
Language : English
ISBN-13 : 9781789957211
Category :
Languages :
Tools :

Product Details

Publication date : Dec 21, 2018
Length: 764 pages
Edition : 1st
Language : English
ISBN-13 : 9781789957211
Category :
Languages :
Tools :

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Table of Contents

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