Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras

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Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras
Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras
By 作者: George Kyriakides – Konstantinos G. Margaritis
ISBN-10 书号: 1789612853
ISBN-13 书号: 9781789612851
Release Finelybook 出版日期: 2019-07-19
pages 页数: (298 )

$39.99

Book Description to Finelybook sorting

Combine popular machine learning techniques to create ensemble models using Python
Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.
With its hands-on approach, you’ll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you’ll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you’ll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You’ll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.
By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.
What you will learn

Implement ensemble methods to generate models with high accuracy
Overcome challenges such as bias and variance
Explore machine learning algorithms to evaluate model performance
Understand how to construct, evaluate, and apply ensemble models
Analyze tweets in real time using Twitter’s streaming API
Use Keras to build an ensemble of neural networks for the MovieLens dataset
contents
1 A Machine Learning Refresher
2 Getting Started with Ensemble Learning
3 Voting
4 Stacking
5 Bagging
6 Boosting
7 Random Forests
8 Clustering
9 Classifying Fraudulent Transactions
10 Predicting Bitcoin Prices
11 Evaluating Sentiment on Twitter
12 Recommending Movies with Keras
13 Clustering World Happiness

CYBER SECURITY: Ultimate Beginners Guide to Learn the Basics and Effective Methods of Cyber Security

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CYBER SECURITY: Ultimate Beginners Guide to Learn the Basics and Effective Methods of Cyber Security
CYBER SECURITY: Ultimate Beginners Guide to Learn the Basics and Effective Methods of Cyber Security (An Essential Guide to Ethical Hacking for Beginners)
By 作者: MICHAEL STEVEN
ISBN-10 书号: 1691906573
ISBN-13 书号: 9781691906574
Release Finelybook 出版日期: 2019-09-08
pages 页数: (127 )

$9.97

Book Description to Finelybook sorting

Protecting yourself and your data from online attacks and hacking has never been more important than and you know what they always say, knowledge is power.
The Principles of Cybersecurity and Hacking series aims to provide you exactly with that knowledge, and with that power. This comprehensive, in-depth guide on the fundamentals, concepts and strategies of Cybersecurity and Hacking will take you to another level of protection in this digital world. It provides you with everything you need to know starting as a Beginner:
This book is in two parts, you will learn and understand topics such as:
1. Understanding Cyber security

Cyber security Attacks All
What Cyber security Management, Planners, And Governance Experts Should Do
Cyber-security educational program: who needs my data?
The Cybersecurity Commandments: On the Small Causes of Big Problems
New US Cybersecurity Strategies
2. Understanding how Hacking is done:

Ethical Hacking for Beginners
Hack Back! A Do-It-Yourself
And there’s so much more to learn, which you will all find in this book!
Hacking is real, and what better way to protect yourself than being pro-active and arming yourself with the knowledge on how it works and what you can do against it.
Get this book NOW. Hacking is real, and many people know how to do it. You can protect yourself from cyber-attacks by being informed and learning how to secure your computer and other devices.


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CYBER SECURITY Ultimate Beginners Guide to Learn the Basics and Effective Methods of Cyber Security 9781691906574.zip

Neural Networks and Statistical Learning, 2nd Edition

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Neural Networks and Statistical Learning, 2nd Edition
Neural Networks and Statistical Learning
By 作者: Ke-Lin Du – M. N. S. Swamy
ISBN-10 书号: 1447174518
ISBN-13 书号: 9781447174516
Edition 版本: 2nd ed. 2019
Release Finelybook 出版日期: 2019-09-13
pages 页数: (988 )

$159.99

Book Description to Finelybook sorting

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing.
Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include:

multilayer perceptron;
the Hopfield network;
associative memory models;clustering models and algorithms;
the radial basis function network;
recurrent neural networks;
nonnegative matrix factorization;
independent component analysis;
probabilistic and Bayesian networks; and
fuzzy sets and logic.
Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Front Matter
1. Introduction
2. Fundamentals of Machine Learning
3. Elements of Computational Learning Theory
4. Perceptrons
5. Multilayer Perceptrons: Architecture and Error Backpropagation
6. Multilayer Perceptrons: Other Learing Techniques
7. Hopfheld Networks, Simulated Annealing, and Chaotic Neural Networks
8. Associative Memory Networks
9. Clustering : Basic Clustering Models and Algorithms
10. Clustering I: Topics in Clustering
11. Radial Basis Function Networks
12. Recurrent Neural Networks
13. Principal Component Analysis
14. Nonnegative Matrix Factorization
15. Independent Component Analysis
16. Discriminant Analysis
17. Reinforcement Learning
18. Compressed Sensing and Dictionary Learning
19. Matrix Completion
20. Kernel Methods
21. Support Vector Machines
22. Probabilistic and Bayesian Networks
23. Boltzmann Machines
24. Deep Learning
25. Combining Multiple Learners: Data Fusion and Ensemble Learning
26. Introduction to Fuzy Sets and Logic
27. Neurofuzzy Systems
28. Neural Network Circuits and Parallel Implementations
29. Pattern Recognition for Biometrics and Bioinformatics
30. Data Mining
31. Big Data, Cloud Computing, and Internet of Things
Back Matter

Microservices with Docker, Flask, and React

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Microservices with Docker, Flask, and ReactMicroservices with Docker, Flask, and React
By 作者: Michael Herman
Pub Date: 2019
ISBN: n/a
Pages: 577
Language: English
Format: PDF
Size: 44 Mb


Book Description to Finelybook sorting

By the end of this part, you will be able to:

Develop a RESTful API with Flask and Python
Practice Test-Driven Development
Configure and run services locally with Docker
Utilize volumes to mount your code into a Docker container
Run unit and integration tests inside a Docker container
Enable services running in different containers to talk to one another
Work with Python and Flask running inside a Docker container
Run both Nginx and Gunicorn in front of Flask
Deploy Flask, Nginx, Gunicorn, and Postgres to an Amazon EC2 instance


下载地址

Microservices with Docker, Flask, and React.pdf

Implementing SAP S/4HANA: A Framework for Planning and Executing SAP S/4HANA Projects

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Implementing SAP S/4HANA: A Framework for Planning and Executing SAP S/4HANA Projects
Implementing SAP S/4HANA: A Framework for Planning and Executing SAP S/4HANA Projects
By 作者: Sanket Kulkarni
ISBN-10 书号: 1484245199
ISBN-13 书号: 9781484245194
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-09-13
pages 页数: (228 )

$32.99

Book Description to Finelybook sorting

Gain a better understanding of implementing SAP S/4HANA-based digital transformations. This book helps you understand the various components involved in the planning and execution of successful SAP S/4HANA projects. Learn how to ensure success by building a solid business case for SAP S/4HANA up front and track business value generated throughout the implementation. Implementing SAP S/4HANA provides a framework for planning and executing SAP S/4HANA projects by articulating the implementation approach used by different components in SAP S/4HANA implementations.
Whether you are mid-way through the SAP S/4HANA program or about to embark on it, this book will help you throughout the journey. If you are looking for answers on why SAP S/4HANA requires special considerations as compared to a traditional SAP implementation, this book is for you.
What You Will Learn

Understand various components of your SAP S/4HANA project
Forecast and track your success throughout the SAP S/4HANA implementation
Build a solid business case for your SAP S/4HANA program
Discover how the implementation approach varies across these components

Front Matter
1.SAP S/4HANA Overview
2.Scope Areas of SAP S/4HANA
3.Process Effort in S/4HANA
4.Development in S/4HANA
5.Data Conversion in S/4HANA
6.Testing Effort in S/4HANA
7.Technical Architecture Effort in S/4HANA
8.Organizational Change Management in S/4HANA
9.Fiori in S/4HANA
10.Embedded Analytics in S/4HANA
11.Deployment and Governance Strategy for S/4HANA
12.Building a Business Case for S/4HANA
13.Alternative S/4HANA Implementation Approaches
Back Matter

The Complete Python Manual – 3rd Edition 2019

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The Complete Python Manual – 3rd Edition 2019

The Complete Python Manual – 3rd Edition 2019
By 作者: James Gale
Pub Date: 2019
ISBN: n/a
Pages: 163
Language: English
Format: PDF
Size: 104 Mb
Our modern digital world is made up of code. The Internet, our phones, TVs, games consoles, banking and virtually anything else you can think of, that’s connected in some way to the wider world, has some Form of code behind it, driving it and telling it how to behave and what to do.
That code can come in different Forms, called programming languages. Some offer better stability, speed and complex algorithms. Others are designed For use with minimal system resources, in places that are impossible For an engineer to access and repair; the entire space and satellite communications industry runs off clever coding. In short, code surrounds and helps us in virtually every aspect of our lives. What a brilliant time then, to learn how to code; to see how a Few simple lines of code can create something amazing and can interact with you and your technology. Python and C++ are two of the most popular and powerful languages to learn and with the help of this book plus a little imagination, you will soon be able to create amazing code.
Now, turn the page and let’s start learning how to code.


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The Complete Python Manual – 3rd Edition 2019.pdf

Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
By 作者: Nazia Habib
ISBN-10 书号: 1789345804
ISBN-13 书号: 9781789345803
Release Finelybook 出版日期: 2019-04-19
pages 页数: (212 )

$34.99

Book Description to Finelybook sorting

Leverage the power of reward-based training for your deep learning models with Python
Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.
This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as frameworks such as Keras and TensorFlow.
A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning.
By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
What you will learn

Explore the fundamentals of reinforcement learning and the state-action-reward process
Understand Markov decision processes
Get well versed with frameworks such as Keras and TensorFlow
Create and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI Gym
Choose and optimize a Q-Network’s learning parameters and fine-tune its performance
Discover real-world applications and use cases of Q-learning
Contents
Preface
Section 1:Q-Learning:A Roadmap
Chapter 1:Brushing Up on Reinforcement Learning Concepts
Chapter 2:Getting Started with the Q-Learning Algorithm
Chapter 3:Setting Up Your First Environment with OpenAl Gym
Chapter 4:Teaching a Smartcab to Drive Using Q-Learning
Section 2:Building and Optimizing Q-Learning Agents
Chapter 5:Building Q-Networks with TensorFlow
Chapter 6:Digging Deeper into Deep Q-Networks with Keras and TensorFlow
Section 3:Advanced Q-Learning Challenges with Keras,TensorFlow,and OpenAl Gym
Chapter 7:Decoupling Exploration and Exploitation in Multi-Armed Bandits
Chapter 8:Further Q-Learning Research and Future Projects
Assessments
Other Books You May Enjoy
Index

Checking Out with the Payment Request API: A Practical Introduction to the HTML5 Payment Request API using Real-world Examples

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Checking Out with the Payment Request API: A Practical Introduction to the HTML5 Payment Request API using Real-world Examples
Checking Out with the Payment Request API: A Practical Introduction to the HTML5 Payment Request API using Real-world Examples
By 作者: Alex Libby
ISBN-10 书号: 1484251830
ISBN-13 书号: 9781484251836
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-09-14
pages 页数: (249 )

$34.99

Book Description to Finelybook sorting

Quickly create consistent checkouts for use within websites, using the power of the HTML5 Payment Request API. This project-oriented book simplifies the process of creating and manipulating checkouts with the Payment Request API in browsers for websites or online applications, using little more than a text editor or free software.
One of the key concerns of any e-commerce company is ensuring customers complete the checkout process successfully, and for them to return. Unfortunately, many checkouts still suffer from a high level of drop-out. The Payment Request API is an open standard being developed by browser vendors to simplify payments for users with a quick and seamless autofill process enabling a broader set of online payment providers to participate in the market. The API is designed to be easy to implement across all supported browsers, and work with any payment type or service provider.
Checking Out with the Payment Request API equips you with a tool set that you can use to develop future projects, incorporate into your existing workflow and allow you to reduce any dependency on complex, custom-made checkouts that might be prone to failure, or unwieldy to use. You’ll learn how to use the Payment Request API to create consistent checkouts quickly and easily, and work through practical example projects that will help familiarize you with using the API. We live in an age where speed and accuracy are of the essence – add effortless flow to your payments using this book today.
What You’ll Learn

Implement the Payment Request API in a project
Explore some of the options for personalizing it for a project
Gain an appreciation of pointers around user experience and how this affects the API
Understand how to manage issues and security when using the Payment Request API
Work through some example projects, from standalone demos to implementing in frameworks
Front Matter
1.Introducing the APl
2.Setting Up a Basic Checkout
3.Configuring and Customizing Our Checkout
4.Shipping
5.Integrating with a Payment Handler
6.Pulling It All Together
7.Project:Enabling the API in a Framework or CMS
8.Project:The Future of the Web Payments APl
Back Matter

Terraform: Up & Running: Writing Infrastructure as Code, 2nd Edition

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Terraform: Up & Running: Writing Infrastructure as Code, 2nd Edition
Terraform: Up & Running: Writing Infrastructure as Code
By 作者: Yevgeniy Brikman
ISBN-10 书号: 1492046906
ISBN-13 书号: 9781492046905
Edition 版本: 2
Release Finelybook 出版日期: 2019-10-08
pages 页数: (368 )

$59.99

Book Description to Finelybook sorting

Terraform has become a key player in the DevOps world for defining, launching, and managing infrastructure as code (IaC) across a variety of cloud and virtualization platforms, including AWS, Google Cloud, Azure, and more. This hands-on second edition, expanded and thoroughly updated for Terraform version 0.12 and beyond, shows you the fastest way to get up and running.
Gruntwork cofounder Yevgeniy (Jim) Brikman walks you through code examples that demonstrate Terraform’s simple, declarative programming language for deploying and managing infrastructure with a few commands. Veteran sysadmins, DevOps engineers, and novice developers will quickly go from Terraform basics to running a full stack that can support a massive amount of traffic and a large team of developers.

Explore changes from Terraform 0.9 through 0.12, including backends, workspaces, and first-class expressions
Learn how to write production-grade Terraform modules
Dive into manual and automated testing for Terraform code
Compare Terraform to Chef, Puppet, Ansible, CloudFormation, and Salt Stack
Deploy server clusters, load balancers, and databases
Use Terraform to manage the state of your infrastructure
Create reusable infrastructure with Terraform modules
Use advanced Terraform syntax to achieve zero-downtime deployment

1. Why Terraform
2. Getting Started with Terraform
3. How to Manage Terraform State
4. How to Create Reusable Infrastructure with Terraform Modules
5. Terraform Tips and Tricks: Loops, If-Statements, Deployment, and Gotchas
6. Production-Grade Terraform Code
7. How to Test Terraform Code
8. How to Use Terraform as a Team
A. Recommended Reading
Index

Agile Systems Engineering

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Agile Systems Engineering
Agile Systems Engineering
By 作者: Bruce Powel Douglass
ISBN-10 书号: 0128021209
ISBN-13 书号: 9780128021200
Edition 版本: 1
Release Finelybook 出版日期: 2015-10-28
pages 页数: (452 )

$69.95

Book Description to Finelybook sorting

Agile Systems Engineering presents a vision of systems engineering where precise specification of requirements, structure, and behavior meet larger concerns as such as safety, security, reliability, and performance in an agile engineering context.
World-renown author and speaker Dr. Bruce Powel Douglass incorporates agile methods and model-based systems engineering (MBSE) to define the properties of entire systems while avoiding errors that can occur when using traditional textual specifications. Dr. Douglass covers the lifecycle of systems development, including requirements, analysis, design, and the handoff to specific engineering disciplines. Throughout, Dr. Douglass couples agile methods with SysML and MBSE to arm system engineers with the conceptual and methodological tools they need to avoid specification defects and improve system quality while simultaneously reducing the effort and cost of systems engineering.

Identifies how the concepts and techniques of agile methods can be effectively applied in systems engineering context
Shows how to perform model-based functional analysis and tie these analyses back to system requirements and stakeholder needs, and forward to system architecture and interface definition
Provides a means by which the quality and correctness of systems engineering data can be assured (before the entire system is built!)
Explains agile system architectural specification and allocation of functionality to system components
Details how to transition engineering specification data to downstream engineers with no loss of fidelity
Includes detailed examples from across industries taken through their stages, including the “Waldo” industrial exoskeleton as a complex system


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Agile Systems Engineering 9780128021200.pdf