Search Results - (((((((want OR agent) OR arts) OR started) OR cantor) OR anne) OR cartsr) OR deep) algorithms.

Machine Learning Algorithms and Applications. by Srinivas, Mettu

Table of Contents: “…5.3 Delineating the Cornerstones -- 5.4 Deep Learning Architectures -- 5.5 Why is CNN Preferred for Computer Vision Applications? …”
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Learn Unity ML-Agents - Fundamentals of Unity Machine Learning : Incorporate New Powerful ML Algorithms Such As Deep Reinforcement Learning for Games. by Lanham, Micheal

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Deep Learning for the Earth Sciences : A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. by Camps-Valls, Gustau

Table of Contents: “…6.1.4 Evaluation Metrics -- 6.1.4.1 Precision-Recall Curve -- 6.1.4.2 Average Precision and Mean Average Precision -- 6.1.5 Applications -- 6.2 Preliminaries on Object Detection with Deep Models -- 6.2.1 Two-stage Algorithms -- 6.2.1.1 R-CNNs -- 6.2.1.2 R-FCN -- 6.2.2 One-stage Algorithms -- 6.2.2.1 YOLO -- 6.2.2.2 SSD -- 6.3 Object Detection in Optical RS Images -- 6.3.1 Related Works -- 6.3.1.1 Scale Variance -- 6.3.1.2 Orientation Variance -- 6.3.1.3 Oriented Object Detection -- 6.3.1.4 Detecting in Large-size Images -- 6.3.2 Datasets and Benchmark -- 6.3.2.1 DOTA -- 6.3.2.2 VisDrone…”
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Network Routing : Algorithms, Protocols, and Architectures. by Medhi, Deep

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Network-Based Distributed Planning Using Coevolutionary Algorithms. by Subbu, Raj

Table of Contents: “…Combinatorial optimization. 2.3. Evolutionary algorithms. 2.4. Agents. 2.5. Distributed problem solving -- 3. …”
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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

Table of Contents: “…; Installing PyTorch; Digital Ocean; Tunneling in to IPython; Amazon Web Services (AWS); Basic PyTorch operations; Default value initialization; Converting between tensors and NumPy arrays; Slicing and indexing and reshaping; In place operations; Loading data; PyTorch dataset loaders; Displaying an image; DataLoader; Creating a custom dataset; Transforms; ImageFolder; Concatenating datasets; Summary; Chapter 2: Deep Learning Fundamentals…”
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Machine learning with Scala quick start guide : leverage popular machine learning algorithms and techniques and implement them in Scala / by Karim, Md. Rezaul

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Theory of randomized search heuristics : foundations and recent developments

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Hands-On Intelligent Agents with OpenAI Gym : Your Guide to Developing AI Agents Using Deep Reinforcement Learning. by Palanisamy, Praveen

Table of Contents: “…Practical applications of reinforcement and deep reinforcement learning algorithmsSummary; Chapter 3: Getting Started with OpenAI Gym and Deep Reinforcement Learning; Code repository, setup, and configuration; Prerequisites; Creating the conda environment; Minimal install -- the quick and easy way; Complete install of OpenAI Gym learning environments; Instructions for Ubuntu ; Instructions for macOS; MuJoCo installation; Completing the OpenAI Gym setup; Installing tools and libraries needed for deep reinforcement learning; Installing prerequisite system packages.…”
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How to guard an art gallery and other discrete mathematical adventures by Michael, T. S., 1960-

Table of Contents: “…s Formula: Second Proof -- 2.8 Batting Averages and Lattice Points -- 2.9 Three Dimensions and N-largements -- 2.10 Notes and References -- 2.11 Problems -- 3 How to Guard an Art Gallery -- 3.1 The Sunflower Art Gallery -- 3.2 Art Gallery Problems -- 3.3 The Art Gallery Theorem -- 3.4 Colorful Consequences -- 3.5 Triangular and Chromatic Assumptions -- 3.6 Modern Art Galleries -- 3.7 Art Gallery Sketches -- 3.8 Right-Angled Art Galleries -- 3.9 Guarding the Guards -- 3.10 Three Dimensions and the Octoplex…”
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Flowchart and Algorithm Basics : The Art of Programming. by Chaudhuri, A. B.

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The Human Algorithm by Solis, Brian

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Algorithms for Dummies. by Mueller, John Paul

Table of Contents: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Algorithms -- Chapter 1 Introducing Algorithms -- Describing Algorithms -- The right way to make toast: Defining algorithm uses -- Finding algorithms everywhere -- Using Computers to Solve Problems -- Getting the most out of modern CPUs and GPUs -- Working with special-purpose chips -- Networks: Sharing is more than caring -- Leveraging available data…”
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Algorithmic composition : a guide to composing music with Nyquist by Simoni, Mary Hope

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Permutation Group Algorithms by Seress, kos

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Deep Learning : From Big Data to Artificial Intelligence with R. by Tuffery, Stephane S.

Table of Contents: “…Cover -- Title Page -- Copyright -- Contents -- Acknowledgements -- Introduction -- Chapter 1 From Big Data to Deep Learning -- 1.1 Introduction -- 1.2 Examples of the Use of Big Data and Deep Learning -- 1.3 Big Data and Deep Learning for Companies and Organizations -- 1.3.1 Big Data in Finance -- 1.3.1.1 Google Trends -- 1.3.1.2 Google Trends and Stock Prices -- 1.3.1.3 The quantmod Package for Financial Analysis -- 1.3.1.4 Google Trends in R -- 1.3.1.5 Matching Data from quantmod and Google Trends -- 1.3.2 Big Data and Deep Learning in Insurance…”
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Autonomous agents and multi-agent systems : explorations in learning, self-organization, and adaptive computation by Liu, Jiming, 1962-

Table of Contents: “…Learning. 1.4. Neural agents. 1.5. Evolutionary agents. 1.6. Learning in cooperative agents. 1.7. …”
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Open Data Structures : an Introduction. by Morin, Pat

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Swarm intelligence and bio-inspired computation : theory and applications

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Advanced Methods and Technologies for Agent and Multi-Agent Systems. by Edited by

Table of Contents: “…; ""Coordination, Cooperation and Teamwork""; ""Obligation and Prohibition Norms Mining Algorithm for Normative Multi-agent Systems""…”
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