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Explorations in Quantum Computing (Texts in Computer Science)
Explorations in Quantum Computing (Texts in Computer Science)

In the decade since the publication of the first edition of “Explorations in Quantum Computing” the field has blossomed into a rich and diverse body of knowledge, and tremendous progress has been made on building functional quantum computer hardware. Yet I find that a discussion of applications of quantum computers still remains...

Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow

Implement state-of-the-art deep reinforcement learning algorithms using Python and its powerful libraries

Key Features

  • Implement Q-learning and Markov models with Python and OpenAI
  • Explore the power of TensorFlow to build self-learning models
  • Eight AI...
Topology of Digital Images: Visual Pattern Discovery in Proximity Spaces
Topology of Digital Images: Visual Pattern Discovery in Proximity Spaces

This book carries forward recent work on visual patterns and structures in digital images and introduces a near set-based a topology of digital images. Visual patterns arise naturally in digital images viewed as sets of non-abstract points endowed with some form of proximity (nearness) relation. Proximity relations make it possible to...

Topological Algorithms for Digital Image Processing (Machine Intelligence and Pattern Recognition)
Topological Algorithms for Digital Image Processing (Machine Intelligence and Pattern Recognition)
Objects in three dimensions, and their two-dimensional images, are approximated digitally by sets of voxels ("volume elements") or pixels ("picture elements"), respectively. Digital geometry is the study of geometric properties of digitized objects (or digitized images of objects); it deals both with the definitions of such...
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

Your one-stop guide to learning and implementing artificial neural networks with Keras effectively

Key Features

  • Design and create neural network architectures on different domains using Keras
  • Integrate neural network models in your applications using this highly practical...
OpenGL 4 Shading Language Cookbook: Build high-quality, real-time 3D graphics with OpenGL 4.6, GLSL 4.6 and C++17, 3rd Edition
OpenGL 4 Shading Language Cookbook: Build high-quality, real-time 3D graphics with OpenGL 4.6, GLSL 4.6 and C++17, 3rd Edition

Over 70 recipes that cover advanced techniques for 3D programming such as lighting, shading, textures, particle systems, and image processing with OpenGL 4.6

Key Features

  • Explore techniques for implementing shadows using shadow maps and shadow volumes
  • Learn to use GLSL...
Learning scikit-learn: Machine Learning in Python
Learning scikit-learn: Machine Learning in Python

Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.

Overview

  • Use Python and scikit-learn to create intelligent applications
  • Apply...
Advanced Image Processing in Magnetic Resonance Imaging (Signal Processing and Communications)
Advanced Image Processing in Magnetic Resonance Imaging (Signal Processing and Communications)
Magnetic Resonance (MR) imaging produces images of the human tissues in a noninvasive manner, revealing the structure, metabolism, and function of tissues and organs. The impact of this image technique in diagnostic radiology is impressive, due to its versatility and flexibility in joining high-quality anatomical images with functional...
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Leverage the power of reinforcement learning techniques to develop self-learning systems using TensorFlow

Key Features

  • Explore reinforcement learning concepts and their implementation using TensorFlow
  • Discover different problem-solving methods for reinforcement...
Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
Mastering OpenCV 4 with Python: A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7

Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.

Key Features

  • Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and...
The Variational Bayes Method in Signal Processing
The Variational Bayes Method in Signal Processing
This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing...
Computational Vision and Medical Image Processing: Recent Trends
Computational Vision and Medical Image Processing: Recent Trends

Nowadays, computationalmethodologies of signal processing and imaging analysis for 2D, 3D and even 4D data are commonly used for various applications in society. For example, Computational Vision systems are progressively used for surveillance tasks, traffic analysis, recognition process, inspection purposes, human-machine interfaces, 3D...

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