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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...
Handbook on Advancements in Smart Antenna Technologies for Wireless Networks (Premier Reference Source)
Handbook on Advancements in Smart Antenna Technologies for Wireless Networks (Premier Reference Source)
The dramatic growth of the wireless communication industry has resulted in searches for new technologies to provide broader bandwidth per user channel, better quality, and new value-added services. Employing smart antennas presents an elegant and relatively economical way to improve the performance of wireless transmission.

The Handbook...

Systems Engineering in Wireless Communications
Systems Engineering in Wireless Communications

This book provides the reader with a complete coverage of radio resource management for 3G wireless communications

Systems Engineering in Wireless Communications focuses on the area of radio resource management in third generation wireless communication systems from a systems engineering perspective. The authors...

Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise...

Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization)
Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization)

Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the...

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In...
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are...
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The...
Jenkins Fundamentals: Accelerate deliverables, manage builds, and automate pipelines with Jenkins
Jenkins Fundamentals: Accelerate deliverables, manage builds, and automate pipelines with Jenkins

Continuous integration with Jenkins speeds up your projects and saves you time and money

Key Features

  • Get a perfect balance of theories and hands-on activities
  • Apply continuous integration and delivery to your workflow
  • Explore concepts such as the plugin...
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed...
The Scrumban [R]Evolution: Getting the Most Out of Agile, Scrum, and Lean Kanban (Agile Software Development Series)
The Scrumban [R]Evolution: Getting the Most Out of Agile, Scrum, and Lean Kanban (Agile Software Development Series)

Create Thriving, High-Performing Teams and Organizations with Scrumban

 

Scrumban allows you to use Kanban as a catalyst for increasingly valuable changes to your existing software development processes, amplifying and expanding upon Scrum’s...

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