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Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0.
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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...
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

Write modern natural language processing applications using deep learning algorithms and TensorFlow

Key Features

  • Focuses on more efficient natural language processing using TensorFlow
  • Covers NLP as a field in its own right to improve understanding for choosing TensorFlow...
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Never Too Old to Get Rich: The Entrepreneur's Guide to Starting a Business Mid-Life
Never Too Old to Get Rich: The Entrepreneur's Guide to Starting a Business Mid-Life

Start a successful business mid-life

When you think of someone launching a start-up, the image of a twenty-something techie probably springs to mind. However, Gen Xers and Baby Boomers are just as likely to start businesses and reinvent themselves later in life. Never Too Old to Get Rich is an exciting...

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....
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...
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...
Topographical Tools for Filtering and Segmentation 1: Watersheds on Node- or Edge-weighted Graphs (Digital Signal and Image Processing)
Topographical Tools for Filtering and Segmentation 1: Watersheds on Node- or Edge-weighted Graphs (Digital Signal and Image Processing)

Mathematical morphology has developed a powerful methodology for segmenting images, based on connected filters and watersheds. We have chosen the abstract framework of node- or edge-weighted graphs for an extensive mathematical and algorithmic description of these tools.

Volume 1 is devoted to watersheds. The
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Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition
Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition

Leverage Docker to deploying software at scale

Key Features

  • Leverage practical examples to manage containers efficiently
  • Integrate with orchestration tools such as Kubernetes for controlled deployments
  • Learn to implement best practices on improving...
The Image Processing Handbook, Sixth Edition
The Image Processing Handbook, Sixth Edition

Image processing is used in a wide variety of applications, for two somewhat different purposes:

1. improving the visual appearance of images to a human observer, including their printing and transmission, and

2. preparing images for the measurement of the features and structures which they reveal.

The...

Raspberry Pi 3 Projects for Java Programmers
Raspberry Pi 3 Projects for Java Programmers

Learn the art of building enticing projects by unleashing the potential of Raspberry Pi 3 using Java

About This Book

  • Explore the small yet powerful mini computer in order to run java applications
  • Leverage Java libraries to build exciting projects on home automation, IoT, and Robotics by...
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