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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More
Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re...

Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms...
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

Insightful projects to master deep learning and neural network architectures using Python and Keras

Key Features

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural...
Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications

Build smart applications by implementing real-world artificial intelligence projects

Key Features

  • Explore a variety of AI projects with Python
  • Get well-versed with different types of neural networks and popular deep learning algorithms
  • Leverage popular...
Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python
Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python

Develop real-world applications powered by the latest advances in intelligent systems

Key Features

  • Gain real-world contextualization using deep learning problems concerning research and application
  • Get to know the best practices to improve and optimize your machine...
OpenCV 3 Computer Vision with Python Cookbook: Leverage the power of OpenCV 3 and Python to build computer vision applications
OpenCV 3 Computer Vision with Python Cookbook: Leverage the power of OpenCV 3 and Python to build computer vision applications

Recipe-based approach to tackle the most common problems in Computer Vision by leveraging the functionality of OpenCV using Python APIs

Key Features

  • Build computer vision applications with OpenCV functionality via Python API
  • Get to grips with image processing, multiple...
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras
Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks

Key Features

  • Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision
  • Combine the power of Python, Keras, and TensorFlow to...
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification

Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral...

R Deep Learning Projects: Master the techniques to design and develop neural network models in R
R Deep Learning Projects: Master the techniques to design and develop neural network models in R

5 real-world projects to help you master deep learning concepts

Key Features

  • Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more
  • Get to grips with R's impressive range of...
Visual Saliency: From Pixel-Level to Object-Level Analysis
Visual Saliency: From Pixel-Level to Object-Level Analysis
This book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc....
Rapid BeagleBoard Prototyping with MATLAB and Simulink
Rapid BeagleBoard Prototyping with MATLAB and Simulink

Leverage the power of BeagleBoard to develop and deploy practical embedded projects

Overview

  • Develop and validate your own embedded audio/video applications rapidly with Beagleboard
  • Create embedded Linux applications on a pure Windows PC
  • Full of illustrations, diagrams, and...
Introduction to Random Signals and Noise
Introduction to Random Signals and Noise

Random signals and noise are present in many engineering systems and networks. Signal processing techniques allow engineers to distinguish between useful signals in audio, video or communication equipment, and interference, which disturbs the desired signal.

With a strong mathematical grounding, this text...

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