Mastering Net Machine Learning

Download Mastering Net Machine Learning full books in PDF, epub, and Kindle. Read online Mastering Net Machine Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Mastering .NET Machine Learning

Author : Jamie Dixon
Publisher : Packt Publishing Ltd
ISBN 13 : 1785881191
Total Pages : 358 pages
Book Rating : 4.7/5 (858 download)

DOWNLOAD NOW!

Book Synopsis Mastering .NET Machine Learning by : Jamie Dixon

Download or read book Mastering .NET Machine Learning written by Jamie Dixon and published by Packt Publishing Ltd. This book was released on 2016-03-29 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the art of machine learning with .NET and gain insight into real-world applications About This Book Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 Set up your business application to start using machine learning techniques Familiarize the user with some of the more common .NET libraries for machine learning Implement several common machine learning techniques Evaluate, optimize and adjust machine learning models Who This Book Is For This book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. What You Will Learn Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 Set up your business application to start using machine learning. Accurately predict the future using regressions. Discover hidden patterns using decision trees. Acquire, prepare, and combine datasets to drive insights. Optimize business throughput using Bayes Classifier. Discover (more) hidden patterns using KNN and Naive Bayes. Discover (even more) hidden patterns using K-Means and PCA. Use Neural Networks to improve business decision making while using the latest ASP.NET technologies. Explore “Big Data”, distributed computing, and how to deploy machine learning models to IoT devices – making machines self-learning and adapting Along the way, learn about Open Data, Bing maps, and MBrace In Detail .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly Style and approach This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your .NET applications with the help of popular machine learning libraries offered by the .NET framework.

Hands-On Machine Learning with ML.NET

Author : Jarred Capellman
Publisher : Packt Publishing Ltd
ISBN 13 : 1789804299
Total Pages : 287 pages
Book Rating : 4.7/5 (898 download)

DOWNLOAD NOW!

Book Synopsis Hands-On Machine Learning with ML.NET by : Jarred Capellman

Download or read book Hands-On Machine Learning with ML.NET written by Jarred Capellman and published by Packt Publishing Ltd. This book was released on 2020-03-27 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core Key FeaturesGet well-versed with the ML.NET framework and its components and APIs using practical examplesLearn how to build, train, and evaluate popular machine learning algorithms with ML.NET offeringsExtend your existing machine learning models by integrating with TensorFlow and other librariesBook Description Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET. What you will learnUnderstand the framework, components, and APIs of ML.NET using C#Develop regression models using ML.NET for employee attrition and file classificationEvaluate classification models for sentiment prediction of restaurant reviewsWork with clustering models for file type classificationsUse anomaly detection to find anomalies in both network traffic and login historyWork with ASP.NET Core Blazor to create an ML.NET enabled web applicationIntegrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detectionWho this book is for If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.

Machine Learning

Author : Jason Bell
Publisher : John Wiley & Sons
ISBN 13 : 1119642191
Total Pages : 497 pages
Book Rating : 4.1/5 (196 download)

DOWNLOAD NOW!

Book Synopsis Machine Learning by : Jason Bell

Download or read book Machine Learning written by Jason Bell and published by John Wiley & Sons. This book was released on 2020-02-17 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.

Deep Learning with C#, .Net and Kelp.Net

Author : Cole Matt R.
Publisher : BPB Publications
ISBN 13 : 9389423740
Total Pages : 388 pages
Book Rating : 4.3/5 (894 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning with C#, .Net and Kelp.Net by : Cole Matt R.

Download or read book Deep Learning with C#, .Net and Kelp.Net written by Cole Matt R. and published by BPB Publications. This book was released on 2019-09-20 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands on with Kelp.Net, Microsoft's latest Deep Learning frameworkKey features Deep Learning Basics The ultimate Kelp.Net reference guide Develop state of the art deep learning applications C# deep learning code Develop advanced deep learning models with minimal code Develop your own advanced deep learning models Loading and Saving Deep Learning Models Comprehensive Kelp.Net reference Sample Deep Learning Models and Tests penCL Reference Easily add deep learning to your applications Many sample models and tests Intuitive and user friendly Description Deep Learning with Kelp.Net is the ultimate reference for C# .Net developers who are passionate about deep learning. Readers will learn all the skills necessary to develop powerful, scalable and flexible deep learning models from a fluid and easy to use API. Upon completing the book the reader will have all the tools necessary to add powerful deep learning capabilities to their new or existing applications.What will you learn In-depth knowledge of Kelp.Net How to develop deep learning models C# deep learning programming Open-Computing Language (OpenCL) Loading and saving deep learning models How to develop and use activation functions How to test deep learning modelsWho this book is for This book targets C# .Net developers who are passionate about deep learning yet want to do so from an easy and intuitive API.Table of contents1. Introduction2. ML/DL Terms and Concepts3. Deep Instrumentation4. Kelp.Net Reference5. Loading and Saving Models6. Model Testing and Training7. Sample Deep Learning Tests8. Creating Your Own Deep Learning Tests9. Appendix A: Evaluation Metrics10. Appendix B: OpenCL About the authorMatt R. Cole is a seasoned developer and published author with over 30 years' experience in Microsoft Windows, C, C++, C# and .Net. Matt is the owner of Evolved AI Solutions, a premier provider of advanced Machine Learning/Bio-AI technologies. Matt developed the first enterprise grade MicroService framework written completely in C# and .Net, which is used in production by a major hedge fund in NYC. Matt also developed the first Bio Artificial Intelligence framework which completely integrates mirror and canonical neurons. He continues to push the limits of Machine Learning, Biological Artificial Intelligence, Deep Learning and MicroServices. In his spare time Matt loves to continue his education and contribute to open source efforts such as Kelp.Net. His Website: www.evolvedaisolutions.comHis LinkedIn Profile: https://www.linkedin.com/in/evolvedai/His Blog: https://evolvedaisolutions.com/blog.html

Mastering Visual Studio .NET

Author : Ian Griffiths
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 9780596003609
Total Pages : 420 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Mastering Visual Studio .NET by : Ian Griffiths

Download or read book Mastering Visual Studio .NET written by Ian Griffiths and published by "O'Reilly Media, Inc.". This book was released on 2003 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book enables intermediate and advanced programmers the kind of depth that's really needed, such as advanced window functionality, macros, advanced debugging, and add-ins, etc. With this book, developers will learn the VS.NET development environment from top to bottom.

Mastering Visual Basic .NET

Author : Evangelos Petroutsos
Publisher : John Wiley & Sons
ISBN 13 : 0782152341
Total Pages : 1112 pages
Book Rating : 4.7/5 (821 download)

DOWNLOAD NOW!

Book Synopsis Mastering Visual Basic .NET by : Evangelos Petroutsos

Download or read book Mastering Visual Basic .NET written by Evangelos Petroutsos and published by John Wiley & Sons. This book was released on 2006-02-20 with total page 1112 pages. Available in PDF, EPUB and Kindle. Book excerpt: VB Programmers: Get in Step with .NET With the introduction of Visual Basic .NET, VB transcends its traditional second-class status to become a full-fledged citizen of the object-oriented programming, letting you access the full power of the Windows platform for the first time. Written bythe author of the best-selling Mastering Visual Basic 6 this all-new edition is the resource you need to make a successful transition to .NET. Comprising in-depth explanations, practical examples, and handy reference information, its coverage includes: Mastering the new Windows Forms Designer and controls Building dynamic forms Using powerful Framework classes such as ArrayLists and HashTables Persisting objects to disk files Handling graphics and printing Achieving robustness via structured exception handling and debugging Developing your own classes and extending existing ones via inheritance Building custom Windows controls Building menus and list controls with custom-drawn items Using ADO.NET to build disconnected, distributed applications Using SQL queries and stored procedures with ADO.NET Facilitating database programming with the visual database tools Building web applications with ASP.NET and the rich web controls Designing web applications to access databases Using the DataGrid and DataList web controls Building XML web services to use with Windows and web applications Special topics like the Multiple Document Interface and powerful recursive programming techniques Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Mastering Machine Learning on AWS

Author : Dr. Saket S.R. Mengle
Publisher : Packt Publishing Ltd
ISBN 13 : 1789347505
Total Pages : 293 pages
Book Rating : 4.7/5 (893 download)

DOWNLOAD NOW!

Book Synopsis Mastering Machine Learning on AWS by : Dr. Saket S.R. Mengle

Download or read book Mastering Machine Learning on AWS written by Dr. Saket S.R. Mengle and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.

Building Machine Learning Powered Applications

Author : Emmanuel Ameisen
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492045063
Total Pages : 243 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Building Machine Learning Powered Applications by : Emmanuel Ameisen

Download or read book Building Machine Learning Powered Applications written by Emmanuel Ameisen and published by "O'Reilly Media, Inc.". This book was released on 2020-01-21 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment

Deep Learning and the Game of Go

Author : Kevin Ferguson
Publisher : Simon and Schuster
ISBN 13 : 1638354014
Total Pages : 611 pages
Book Rating : 4.6/5 (383 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning and the Game of Go by : Kevin Ferguson

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Mastering C# and .NET Framework

Author : Marino Posadas
Publisher : Packt Publishing Ltd
ISBN 13 : 1785885405
Total Pages : 560 pages
Book Rating : 4.7/5 (858 download)

DOWNLOAD NOW!

Book Synopsis Mastering C# and .NET Framework by : Marino Posadas

Download or read book Mastering C# and .NET Framework written by Marino Posadas and published by Packt Publishing Ltd. This book was released on 2016-12-15 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep dive into C# and .NET architecture to build efficient, powerful applications About This Book Uniquely structured content to help you understand what goes on under the hood of .NET's managed code platform to master .NET programming Deep dive into C# programming and how the code executes via the CLR Packed with hands-on practical examples, you'll understand how to write applications to make full use of the new features of .NET 4.6, .NET Core and C# 6/7 Who This Book Is For This book was written exclusively for .NET developers. If you've been creating C# applications for your clients, at work or at home, this book will help you develop the skills you need to create modern, powerful, and efficient applications in C#. No knowledge of C# 6/7 or .NET 4.6 is needed to follow along—all the latest features are included to help you start writing cross-platform applications immediately. You will need to be familiar with Visual Studio, though all the new features in Visual Studio 2015 will also be covered. What You Will Learn Understand C# core concepts in depth, from sorting algorithms to the Big O notation Get up to speed with the latest changes in C# 6/7 Interface SQL Server and NoSQL databases with .NET Learn SOLID principles and the most relevant GoF Patterns with practical examples in C# 6.0 Defend C# applications against attacks Use Roslyn, a self-hosted framework to compile and advanced edition in both C# and Visual basic .NET languages Discern LINQ and associated Lambda expressions, generics, and delegates Design a .NET application from the ground up Understand the internals of a .NET assembly Grasp some useful advanced features in optimization and parallelism In Detail Mastering C# and .NET Framework will take you in to the depths of C# 6.0/7.0 and .NET 4.6, so you can understand how the platform works when it runs your code, and how you can use this knowledge to write efficient applications. Take full advantage of the new revolution in .NET development, including open source status and cross-platform capability, and get to grips with the architectural changes of CoreCLR. Start with how the CLR executes code, and discover the niche and advanced aspects of C# programming – from delegates and generics, through to asynchronous programming. Run through new forms of type declarations and assignments, source code callers, static using syntax, auto-property initializers, dictionary initializers, null conditional operators, and many others. Then unlock the true potential of the .NET platform. Learn how to write OWASP-compliant applications, how to properly implement design patterns in C#, and how to follow the general SOLID principles and its implementations in C# code. We finish by focusing on tips and tricks that you'll need to get the most from C# and .NET. This book also covers .NET Core 1.1 concepts as per the latest RTM release in the last chapter. Style and approach This book uses hands-on practical code examples that will take you into the depths of C# and .NET. Packed with hands-on practical examples, it is great as a tutorial, or as a reference guide.

Mastering .Dot Net Framework

Author : Cybellium Ltd
Publisher : Cybellium Ltd
ISBN 13 :
Total Pages : 209 pages
Book Rating : 4.8/5 (591 download)

DOWNLOAD NOW!

Book Synopsis Mastering .Dot Net Framework by : Cybellium Ltd

Download or read book Mastering .Dot Net Framework written by Cybellium Ltd and published by Cybellium Ltd. This book was released on 2023-09-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cybellium Ltd is dedicated to empowering individuals and organizations with the knowledge and skills they need to navigate the ever-evolving computer science landscape securely and learn only the latest information available on any subject in the category of computer science including: - Information Technology (IT) - Cyber Security - Information Security - Big Data - Artificial Intelligence (AI) - Engineering - Robotics - Standards and compliance Our mission is to be at the forefront of computer science education, offering a wide and comprehensive range of resources, including books, courses, classes and training programs, tailored to meet the diverse needs of any subject in computer science. Visit https://www.cybellium.com for more books.

Introducing Machine Learning

Author : Dino Esposito
Publisher : Microsoft Press
ISBN 13 : 0135588383
Total Pages : 617 pages
Book Rating : 4.1/5 (355 download)

DOWNLOAD NOW!

Book Synopsis Introducing Machine Learning by : Dino Esposito

Download or read book Introducing Machine Learning written by Dino Esposito and published by Microsoft Press. This book was released on 2020-01-31 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library

Deep Learning with Python

Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning with Python by : Francois Chollet

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Understanding Machine Learning

Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
ISBN 13 : 1107057132
Total Pages : 415 pages
Book Rating : 4.1/5 (7 download)

DOWNLOAD NOW!

Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Mastering Machine Learning for Penetration Testing

Author : Chiheb Chebbi
Publisher : Packt Publishing Ltd
ISBN 13 : 178899311X
Total Pages : 264 pages
Book Rating : 4.7/5 (889 download)

DOWNLOAD NOW!

Book Synopsis Mastering Machine Learning for Penetration Testing by : Chiheb Chebbi

Download or read book Mastering Machine Learning for Penetration Testing written by Chiheb Chebbi and published by Packt Publishing Ltd. This book was released on 2018-06-27 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Deep Learning With Python

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 266 pages
Book Rating : 4./5 ( download)

DOWNLOAD NOW!

Book Synopsis Deep Learning With Python by : Jason Brownlee

Download or read book Deep Learning With Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-05-13 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.