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  • Deep learning1909.rar

    This file contains:Deep Learning and Missing Data in Engineering Systems (2019, Springer International Publishing).pdf Deep Learning Classifiers with Memristive Networks_ Theory and Applications (2020, Springer International Publishing).pdf Deep Learning for Dummies (2019, Wiley).pdf Deep Learning for NLP and Speech Recognition (2019, Springer International Publishing).pdf Deep Learning for Search (2019, Manning Publications).pdf Deep Learning for the Life Sciences_ Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More (2019, O’Reilly Media).pdf Deep Learning Through Sparse and Low-Rank Modeling (2019, Academic Press).pdf Deep Learning with R (2019, Springer).pdf Deep Learning_ Convergence to Big Data Analytics (2019, Springer Singapore).pdf Deep Learning_ Fundamentals, Theory and Applications (2019, Springer International Publishing).pdf Deep Reinforcement Learning_ Frontiers of Artificial Intelligence (2019, Springer).pdf Fog Computing, Deep Learning and Big Data Analytics-Research Directions (2019, Springer Singapore).pdf Generative Deep Learning_ Teaching Machines to Paint, Write, Compose, and Play (2019, O’Reilly Media).epub Generative Deep Learning_ Teaching Machines to Paint, Write, Compose, and Play (2019, O’Reilly Media).pdf Handbook of Deep Learning Applications (2019, Springer).pdf Hands-On Deep Learning with Go (2019, Packt Publishing).epub Hands-On Deep Learning with Go (2019, Packt Publishing).pdf Hands-On Java Deep Learning for Computer Vision - Implement machine learning and neural network methodologies to perform computer vision-related tasks. (2019, Packt).pdf Hands-On Neural Networks with Keras_ Design and create neural networks using deep learning and artificial intelligence principles (2019, Packt Publishing).epub Introduction to Deep Learning (2019, The MIT Press).pdf Learn Computer Vision Using OpenCV_ With Deep Learning CNNs and RNNs (2019, Apress).pdf Learn PySpark. Build Python-based Machine Learning and Deep Learning Models (2019, Apress).pdf Machine Learning and Data Mining in Aerospace Technology (2020, Springer International Publishing).pdf Mastering OpenCV 4 with Python _ A Practical Guide Covering Topics from Image Processing, Augmented Reality to Deep Learning with OpenCV 4 and Python 3. 7. (2019, Packt Publis.pdf Mastering OpenCV 4 with Python _ A Practical Guide Covering Topics from Image Processing, Augmented Reality to Deep Learning with OpenCV 4 and Python 3. 7. (2019, Packt Publishing.epub Recent Advances in Big Data and Deep Learning_ Proceedings of the INNS Big Data a.pdf Recent Developments in Machine Learning and Data Analytics_ IC3 2018 (2019, Springer.pdf Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks-Springer Singapore (2019).pdf

    2019-09-25
    8
  • Internet of things合集

    this file contains:5G LTE Narrowband Internet of Things (NB-IoT)-CRC Press (2019).pdf Artificial Intelligence in IoT-Springer International Publishing (2019).pdf Beginning LoRa Radio Networks with Arduino_ Build Long Range, Low Power Wireless IoT Networks-Apress (2019).pdf Cognitive Computing for Big Data Systems Over IoT Frameworks, Tools and Applications.pdf Compact and Fast Machine Learning Accelerator for IoT Devices-Springer Singapore (2019).pdf Creating Smart Enterprises_ Leveraging Cloud, Big Data, Web, Social Media, Mobile and IoT Technologies-Auerbach Publications_CRC Press (2018).pdf Cyber Physical Computing for IoT-driven Services -Springer International Publishing (2018).pdf Defending IoT Infrastructures with the Raspberry Pi_ Monitoring and Detecting Nefarious Behavior in Real Time-Apress (2018).pdf Delphi Cookbook, _ Recipes to Master Delphi for IoT Integrations, Cross-Platform, Mobile and Server-side Development, 3rd Edition.-Packt Publishing Ltd (2018).pdf Design, Launch, and Scale IoT Services_ A Practical Business Approach-Apress (2018).pdf Designing Embedded Systems and the Internet of Things (IoT) with the ARM? Mbed?-Wiley (2018).pdf Guide to Ambient Intelligence in the IoT Environment_ Principles, Technologies and Applications-Springer International Publishing (2019).pdf Handbook of Big Data and IoT Security-Springer International Publishing (2019).pdf Hybrid ADCs, smart sensors for the IoT, and sub-1V & advanced node analog circuit design _ advances in analog circuit design 2017-Springer.pdf Internet of things (IoT) _ technologies, applications, challenges and solutions-CRC Press_Taylor & Francis (2018).pdf Internet of Things Programming Projects Build modern IoT solutions with the Raspberry Pi 3 and Python.pdf Internet-of-Things (IoT) Systems_ Architectures, Algorithms, Methodologies (2018, Springer International Publishing).pdf IoT and Low-Power Wireless _ Circuits, Architectures, and Techniques-CRC Press (2018).pdf IoT Attack Handbook_ A Field Guide to Understanding IoT Attacks from the Mirai Botnet to Its Modern Variants-Radware (2018).pdf IoT for Smart Grids_ Design Challenges and Paradigms-Springer International Publishing (2019).pdf IoT, AI and Blockchain for .NET-Apress (2018).pdf Machine Learning and Iot_ A Biological Perspective-CRC Press (2019).pdf Multimedia-Enabled Sensors in Iot_ Data Delivery and Traffic Modelling-CRC Press (2018).pdf Practical Guide to LTE-A, VoLTE and IoT_ Paving the way towards 5G-John Wiley and Sons, Inc (2018).pdf Raspberry Pi for Arduino Users_ Building IoT and Network Applications and Devices-Apress (2018).pdf Recent Trends and Advances in Wireless and IoT-enabled Networks-Springer International Publish.pdf Secure Compressive Sensing in Multimedia Data, Cloud Computing and IoT (2019, Springer Singapore).pdf Smart Sensors and Systems_ Innovations for Medical, Environmental, and IoT Applications-Springer International Publishing (2017).pdf The IoT Hacker’s Handbook_ A Practical Guide to Hacking the Internet of Things-Apress (2019).pdf The IoT Physical Layer_ Design and Implementation-Springer International Publishing (2019).pdf Ubiquitous Computing and Computing Security of IoT (2019, Springer International Publishing).pdf Ultra Low Power ECG Processing System for IoT Devices (2019, Springer International Publishing).pdf Wireless Powered Communication Networks_ From Security Challenges to IoT Applications (2019, Springer International Publishing).pdf

    2019-05-18
    17
  • Reinforcement learning合集

    this file contains:Advanced Deep Learning with Keras_ Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018, Packt Publishing.pdf Deep Reinforcement Learning for Wireless Networks (2019, Springer International Publishing).pdf Deep Reinforcement Learning Hands-On_ Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more.pdf Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).epub Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).pdf Keras Reinforcement Learning Projects (2018, Packt Publishing).epub Keras Reinforcement Learning Projects (2018, Packt Publishing).pdf Practical Reinforcement Learning Develop self-evolving, intelligent agents with OpenAI Gym, Python and Java.pdf Python Reinforcement Learning Projects - 2018.pdf Reinforcement Learning for Optimal Feedback Control (2018, Springer International Publishing).pdf Reinforcement Learning with TensorFlow_ A beginner’s guide to designing self-learning systems with TensorFlow and OpenAI Gym (2018, Packt Publishing).pdf Reinforcement Learning _ With Open AI, TensorFlow and Keras Using Python-Apress (2018).pdf Reinforcement Learning_ An Introduction (2018, The MIT Press).pdf Simulation-Based Optimization_ Parametric Optimization Techniques and Reinforcement Learning (2015, Springer US).pdf Statistics for Machine Learning_ Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R-Packt Publishing (2017).pdf Tensorflow for Deep Learning_ From Linear Regression to Reinforcement Learning (2018, O'Reilly Media).pdf

    2019-04-25
    14
  • 机器学习合集201904

    this file contains:Ensemble Machine Learning Cookbook - 2019.pdf Hands-On Artificial Intelligence for IoT - 2019.pdf Home Automation with Raspberry Pi_ Projects Using Google Home, Amazon Echo, and Other Intelligent Personal Assistants (2019, McGraw-Hill Education).pdf Intelligent Projects Using Python - 2019.pdf Machine Learning and AI for Healthcare. Big Data for improved Health Outcomes (2019, Apress).pdf Practical Machine Learning and Image Processing - 2019.pdf Practical Python AI Projects.pdf Practical Recommender Systems (2019, Manning Publications).pdf Python Artificial Intelligence Projects for Beginners_ Get up and running with Artificial Intelligence using 8 smart and exciting AI applications (2018, Packt Publishing).pdf Python Machine Learning Blueprints 2nd - 2019.pdf Python Reinforcement Learning Projects - 2018.pdf Python Robotics Projects_ Build smart and collaborative robots using Python (2018, Packt Publishing).pdf Python_ Beginner’s Guide to Artificial Intelligence (2018, Packt Publishing).epub Python_ Beginner’s Guide to Artificial Intelligence (2018, Packt Publishing).pdf Pytorch Recipes_ A Problem-Solution Approach (2019, Apress).pdf Simple Machine Learning for Programmers - 2018.pdf TensorFlow 2 Machine Learning Cookbook (2018, Packt).epub TensorFlow 2 Machine Learning Cookbook (2018, Packt).pdf

    2019-04-15
    10
  • Zabbix Enterprise Network Monitoring Made Easy PacktPub (2017)

    Nowadays, monitoring systems play a crucial role in any IT environment. They are extensively used to not only measure your system's performance, but also to forecast capacity issues. This is where Zabbix, one of the most popular monitoring solutions for networks and applications, comes into the picture. With an efficient monitoring system in place, you'll be able to foresee when your infrastructure runs under capacity and react accordingly. Due to the critical role a monitoring system plays, it is fundamental to implement it in the best way from its initial setup. This avoids misleading, confusing, or, even worse, false alarms that can disrupt an efficient and healthy IT department.

    2019-04-15
    7
  • mathematica合集

    this file contains:A Mathematica Primer for Physicists-CRC Press (2018).pdf An Elementary Introduction to the Wolfram Language 2ed.pdf An Engineer's guide to Mathematica.pdf An Introduction to Programming with mma.pdf Classical Mechanics with Mathematica?-Birkh?user (2018).pdf CRC standard curves and surfaces with Mathematica-CRC Press (2016).pdf Dynamical Systems with Applications Using Mathematica.pdf Essentials of Programming in Mathematica.pdf Foundations of Fluid Mechanics with Applications Problem Solving Using Mathematica.pdf Geographical Models with Mathematica- ISTE Press - Elsevier (2017).pdf Geometric Optics_ Theory and Design of Astronomical Optical Systems Using Mathematica.pdf Group Theory in Solid State Physics and Photonics Problem Solving with Mathematica.pdf Groups and Manifolds_ Lectures for Physicists with Examples in Mathematica (2017, de Gruyter).pdf HANDS-ON START TO WOLFRAM 2016.pdf Introduction to mma with Applications.pdf Irreducibility and Computational Equivalence 10 Years After Wolfram's A New Kind of Science.pdf Mathematica Beyond Mathematics. The Wolfram Language in the Real World.pdf Mathematica by Example 5 Edition-Academic Press (2017).pdf Mathematica介绍及数学建模中的应用.pdf mma for Bioinformatics. A Wolfram Language Approach to Omics-Springer (2018).pdf Molecular Physical Chemistry_ A Computer-based Approach using Mathematica? and Gaussian-Springer International Publishing (2017).pdf Raspbian OS Programming with the Raspberry Pi_ IoT Projects with Wolfram, Mathematica, and Scratch-Apress (2019).pdf Schaum's Outline of Mathematica and the wolfram language.pdf

    mma

    2019-04-13
    11
  • Schaum's Outline of Mathematica and the wolfram language 2019

    This book is designed to help students and professionals who use mathematics in their daily routine learn Mathematica®, a computer system designed to perform complex mathematical calculations. My approach is simple: learn by example. Along with easy to read descriptions of the most widely used commands, I have included a collection of over 750 examples and solved problems, each specifically designed to illustrate an important feature of the Mathematica software. No attempt has been made to discuss all the capabilities of Mathematica. As this is a book designed for first-time users at the undergraduate level, I have included those commands and options that are most commonly used in algebra, trigonometry, calculus, differential equations, and linear algebra. Most examples and solved problems are short and to the point. Comments have been included, where appropriate, to clarify what might be confusing to the reader. The reader is encouraged not only to replicate the output shown in the text, but to make modifications and investigate the resulting effect upon the output. I have found this to be the most effective way to learn the syntax and capabilities of this truly unique program. Over the years Mathematica has undergone a significant number of changes. This third edition incorporates all the changes in the command descriptions, examples, and solved problems. In addition, a comprehensive list of commands used in the book together with their descriptions is conveniently located in the appendix. The first three chapters serve as an introduction to the syntax and style of Mathematica. The structure of the remainder of the book is such that the reader need only be concerned with those chapters of interest to him or her. If, on occasion, a command is encountered that has been discussed in a previous chapter, the index may be used to conveniently locate the command’s description. Without a doubt you will be impressed with Mathematica’s capabilities. It is my sincere hope that you will use the power built into this software to investigate the wonders of mathematics in a way that would have been impossible just a few years ago.

    mma

    2019-04-12
    7
  • Geometric Optics_ Theory and Design of Astronomical Optical Systems Using mma

    A very wide selection of excellent books are available to the reader interested in geometric optics. Roughly speaking, these texts can be divided into three main classes. In the first class (see, for instance, [1–14]), we find books that present the theoretical aspects of the subject, usually starting from the Lagrangian and Hamiltonian formulations of geometric optics. These texts analyze the relations between geometric optics, mechanics, partial differential equations, and the wave theory of optics. The second class comprises books that focus on the applications of this theory to optical instruments. In these books some essential formulae, which are reported without proofs, are used to propose exact or approximate solutions to real-world problems (an excellent example of this class is represented by [26]). The third class contains books that approach the subject in a manner that is intermediate between the first two classes (see, for instance, [15–21]). The aim of this book, which could be placed in the third class, is to provide the reader with the mathematical background needed to design many optical combinations that are used in astronomical telescopes and cameras.1 The results presented here were obtained by using a different approach to third-order aberration theory as well as the extensive use of the software package Mathematica®. The third-order approach to third-order aberration theory adopted in this book is based on Fermat’s principle and on the use of particular optical paths (not rays) termed stigmatic paths. This approach makes it easy to derive the third-order aberration formulae. In this way, the reader is able to understand and handle the formulae required to design optical combinations without resorting to the much more complex Lagrangian and Hamiltonian formalisms and Seidel’s relations. On the other hand, the Lagrangian and Hamiltonian formalisms have unquestionable theoretical utility considering their important applications in optics, mechanics, and the theory of partial differential equations. For this reason the Lagrangian and Hamiltonian optics are widely discussed in Chapters 10–12.

    mma

    2019-04-12
    5
  • Natural Language Processing with PyTorch_ Build Intelligent Language App

    This book aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground. While writing the book, we had to make difficult, and sometimes uncomfortable, choices on what material to leave out. For a beginner reader, we hope the book will provide a strong foundation in the basics and a glimpse of what is possible. Machine learning, and deep learning in particular, is an experiential discipline, as opposed to an intellectual science. The generous endtoend code examples in each chapter invite you to partake in that experience. When we began working on the book, we started with PyTorch 0.2. The examples were revised with each PyTorch update from 0.2 to 0.4. P yTorch 1.0 is due to release around when this book comes out. The code examples in the book are PyTorch 0.4–compliant and should work as they are with the upcoming PyTorch 1.0 release. A note regarding the style of the book. We have intentionally avoided mathematics in most places, not because deep learning math is particularly difficult (it is not), but because it is a distraction in many situations from the main goal of this book—to empower the beginner learner. Likewise, in many cases, both in code and text, we have favored exposition over succinctness. Advanced readers and experienced programmers will likely see ways to tighten up the code and so on, but our choice was to be as explicit as possible so as to reach the broadest of the audience that we want to reach.

    NLP

    2019-04-12
    8
  • Advances in Deep Learning 2019

    This book discusses the state-of-the-art deep learning models used by researchers recently. Various deep architectures and their components are discussed in detail. Algorithms that are used to train deep architectures with fast convergence rate are illustrated with applications. Various fine-tuning algorithms are discussed for optimizing the deep models. These deep architectures not only are capable of learning complex tasks but can even outperform humans in some dedicated applications. Despite the remarkable advances in this area, training deep architectures with a huge number of hyper-parameters is an intricate and ill-posed optimization problem. Various challenges are outlined at the end of each chapter. Another issue with deep architectures is that learning becomes computationally intensive when large volumes of data are used for training. The book describes a transfer learning approach for faster training of deep models. The use of this approach is demonstrated in fingerprint datasets. The book is organized into eight chapters: Chapter 1 starts with an introduction to machine learning followed by fundamental limitations of traditional machine learning methods. It introduces deep networks and then briefly discusses why to use deep learning and how deep learning works. Chapter 2 of the book is dedicated to one of the most successful deep learning techniques known as convolutional neural networks (CNNs). The purpose of this chapter is to give its readers an in-depth but easy and uncomplicated explanation of various components of convolutional neural network architectures. Chapter 3 discusses the training and learning process of deep networks. The aim of this chapter is to provide a simple and intuitive explanation of the backpropagation algorithm for a deep learning network. The training process has been explained step by step with easy and straightforward explanations. Chapter 4 focuses on various deep learning architectures that are based on CNN. It introduces a reader to block diagrams of these architectures. It discusses how deep learning architectures have evolved while addressing the limitations of previous deep learning networks. v Chapter 5 presents various unsupervised deep learning architectures. The basics of architectures and associated algorithms falling under the unsupervised category are outlined. Chapter 6 discusses the application of supervised deep learning architecture for face recognition problem. A comparison of the performance of supervised deep learning architecture with traditional face recognition methods is provided in this chapter. Chapter 7 focuses on the application of convolutional neural networks (CNNs) for fingerprint recognition. This chapter extensively explains automatic fingerprint recognition with complete details of the CNN architecture and methods used to optimize and enhance the performance. In addition, a comparative analysis of deep learning and non-deep learning methods is presented to show the performance difference. Chapter 8 explains how to apply the unsupervised deep networks to handwritten digit classification problem. It explains how to build a deep learning model in two steps, where unsupervised training is performed during the first step and supervised fine-tuning is carried out during the second step.

    DL

    2019-04-12
    11
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