Home | Amazing | Today | Tags | Publishers | Years | Search 
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

Key Features

  • Explore deep reinforcement learning (RL), from the first principles to the latest algorithms
  • Evaluate high-profile RL methods, including value...
Computer Vision: Models, Learning, and Inference
Computer Vision: Models, Learning, and Inference

There are already many computer vision textbooks, and it is reasonable to question the need for another. Let me explain why I chose to write this volume.

Computer vision is an engineering discipline; we are primarily motivated by the real-world concern of...

Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)
Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning)

We have been very pleased, beyond our expectations, with the reception of the first edition of this book. Bioinformatics, however, continues to evolve very rapidly, hence the need for a new edition. In the past three years, fullgenome sequencing has blossomed with the completion of the sequence of the fly and the first draft of the...

Energy Minimization Methods in Computer Vision and Pattern Recognition: 8th International Conference, EMMCVPR 2011
Energy Minimization Methods in Computer Vision and Pattern Recognition: 8th International Conference, EMMCVPR 2011

Over the last few decades, energy minimization methods have become an established paradigm to resolve a variety of challenges in the fields of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed a very limited...

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques

This volume contains the papers presented at the 14th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2011) and the 15th International Workshop on Randomization and Computation (RANDOM 2011), which took place concurrently in Princeton University, USA, during August 17–19,...

Word Processing in Groups
Word Processing in Groups

Connections between the theory of hyperbolic manifolds and the theory of automata are deeply interwoven in the history of mathematics of this century.

The use of symbol sequences to study dynamical systems originates in the work of Kocbe [Koc27, Koe29] and Morse [Mor87j, who both used symbol saliences to code geodesies on a...

Anticipatory Optimization for Dynamic Decision Making (Operations Research/Computer Science Interfaces Series)
Anticipatory Optimization for Dynamic Decision Making (Operations Research/Computer Science Interfaces Series)

Anticipatory optimization for dynamic decision making relies on a number of different scientific disciplines. On a general level, the foundations of the field may be localized at the intersection of operations research, computer science and decision theory. Closer inspection reveals the important role of branches such as simulation,...

Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
Understanding Computational Bayesian Statistics (Wiley Series in Computational Statistics)
In theory, Bayesian statistics is very simple. The posterior is proportional to the prior times likelihood. This gives the shape of the posterior, but it is not a density so it cannot be used for inference. The exact scale factor needed to make this a density can be found only in a few special cases. For other cases, the scale...
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...

Approximate Iterative Algorithms
Approximate Iterative Algorithms

Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and convergence rates, and for the optimal design of...

Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction

Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete...

Operations Research: A Model-Based Approach (Springer Texts in Business and Economics)
Operations Research: A Model-Based Approach (Springer Texts in Business and Economics)

The book covers the standard models and techniques used in decision making in organizations. The main emphasis of the book is on modeling business-related scenarios and the generation of decision alternatives. Fully solved examples from many areas are used to illustrate the main concepts without getting bogged down in technical details. The...

Result Page: 11 10 9 8 7 6 5 4 3 2 
©2024 LearnIT (support@pdfchm.net) - Privacy Policy