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Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

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Reinforcement and Systemic Machine Learning for Decision Making
Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement and Systemic Machine Learning for Decision Making

There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the...

CCNA Routing and Switching Complete Review Guide: Exam 100-105, Exam 200-105, Exam 200-125
CCNA Routing and Switching Complete Review Guide: Exam 100-105, Exam 200-105, Exam 200-125

Tight, focused CCNA review covering all three exams

The CCNA Routing and Switching Complete Review Guide offers clear, concise review for Exams 100-105, 200-105, and 200-125. Written by best-selling certification author and Cisco guru Todd Lammle, this guide is your ideal resource for quick review and reinforcement of...

Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control
Modern day society relies on the operation of complex systems including aircraft, automobiles, electric power systems, economic entities, business organizations, banking and finance systems, computer networks, manufacturing systems, and industrial processes, Decision and control are responsible for ensuring that these systems perform...
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
Algorithmic Learning Theory: 15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
This book constitutes the refereed proceedings of the 15th International Conference on Algorithmic Learning Theory, ALT 2004, held in Padova, Italy in October 2004.

The 29 revised full papers presented together with 5 invited papers and 3 tutorial summaries were carefully reviewed and selected from 91 submissions. The papers are organized in...

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...

Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)
Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)
Teaching and learning are difficult tasks not only when people are involved but also with regard to computer programs and machines: When the teaching/learning units are too small, we cannot express sufficient context to teach a differentiated lesson; when they are too large, the complexity of the learning task can increase...
Foundations of Learning Classifier Systems (Studies in Fuzziness and Soft Computing)
Foundations of Learning Classifier Systems (Studies in Fuzziness and Soft Computing)
Learning Classifier Systems (LCS) [Holland, 1976] are a machine learning technique which combines evolutionary computing, reinforcement learning, supervised learning or unsupervised learning, and heuristics to produce adaptive systems. They are rulebased systems, where the rules are usually in the traditional production system form of “IF...
The CEH Prep Guide: The Comprehensive Guide to Certified Ethical Hacking
The CEH Prep Guide: The Comprehensive Guide to Certified Ethical Hacking
A benchmark guide for keeping networks safe with the Certified Ethical Hacker program

Seasoned authors Ronald Krutz and Russell Dean Vines continue in the tradition of their CISSP security franchise by bringing you this comprehensive guide to the Certified Ethical Hacker (CEH) program. Serving as a valuable tool for...

Data Structures and Algorithms in Python
Data Structures and Algorithms in Python

Based on the authors’ market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures....

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...
Computational Intelligence in Medical Imaging: Techniques and Applications
Computational Intelligence in Medical Imaging: Techniques and Applications

CI Techniques & Algorithms for a Variety of Medical Imaging Situations

Documents recent advances and stimulates further research

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent...

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