Moolayil (33 Ergebnisse)

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.…

Zustand: New.

Zustand: New.

Paperback. Zustand: New. Second Edition. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.What You'll LearnReview machine learning fundamentals such as overfitting, underfitting, and regularization.Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.Apply in-depth linear algebra with PyTorchExplore PyTorch fundamentals andits building blocksWork with tuning and optimizing models Who This Book Is ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.…

Paperback. Zustand: New. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.What You'll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.…

Paperback. Zustand: New.

Zustand: New.

Zustand: New.

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Zustand: As New. Unread book in perfect condition.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: Brand New. 182 pages. 9.00x6.00x0.50 inches. In Stock.

Zustand: New.

Zustand: New.

Paperback. Zustand: New. Enter the world of Internet of Things with the power of data science with this highly practical, engaging bookAbout This Book. Explore real-world use cases from the Internet of Things (IoT) domain using decision science with this easy-to-follow, practical book. Learn to make smarter decisions on top of your IoT solutions so that your IoT is smart in a real sense. This highly practical, example-rich guide fills the gap between your knowledge of data science and IoTWho This Book Is ForIf you have a basic programming experience with R and want to solve business use cases in IoT using decision science then this book is for you. Even if your're a non-technical manager anchoring IoT projects, you can skip the code and still benefit from the book.What You Will Learn. Explore decision science with respect to IoT. Get to know the end to end analytics stack - Descriptive + Inquisitive + Predictive + Prescriptive. Solve problems in IoT connected assets and connected operations. Design and solve real-life IoT business use cases using cutting edge machine learning techniques. Synthesize and assimilate results to form the perfect story for a business. Master the art of problem solving when IoT meets decision science using a variety of statistical and machine learning techniques along with hands on tasks in RIn DetailWith an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.By the end of this book, you'll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for itStyle and approachThis scenario-based tutorial approaches the topic systematically, allowing you to build upon what you learned in previous chapters.…

Zustand: New. pp. 245.

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Paperback. Zustand: New. Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.Key FeaturesStudy supervised learning algorithms by using real-world datasetsFine-tune optimal parameters with hyperparameter optimizationSelect the best algorithm using the model evaluation frameworkBook DescriptionR provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.What you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search, and Bayesian optimizationDeploy your model on Amazon Web Services (AWS) Lambda with plumberImprove your model's performance with feature selection and dimensionality reductionWho this book is forThis book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.…

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Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Neuware - In Grace in the Climb, Samuel G. Moolayil shares his deeply personal journey of faith, resilience, and divine grace. From his humble beginnings in the monsoon-soaked hills of Kerala, India, to navigating the challenges of immigration and decades of public service in America, Samuel's story is a testament to God's unwavering faithfulness. Through seasons of loss, including the heartbreaking death of his firstborn son, and moments of triumph, such as the blessings of family and professional milestones, Samuel reflects on how God's grace sustained him through every valley and ascent. ?After the devastating loss of their firstborn son, Samuel and his wife Jessy encountered God in a life-changing way-not in ease, but in surrender. Through that valley of grief came a profound awakening: the redeeming love of Christ, the power of the Holy Spirit, and the steadfast faithfulness of God that carried them forward.Tender, authentic, and brimming with spiritual depth, Grace in the Climb reminds us that valleys are not dead ends but pathways upward-that surrender births strength, and that God's grace is sufficient for every step of the climb.This heartfelt memoir is not just a recounting of life's struggles and victories-it is a spiritual testimony of surrender, hope, and the transformative power of God's love. Grace in the Climb reminds readers that life's hardest climbs are often where grace is most profoundly revealed. ? Samuel G. Moolayil is a retired Chief Financial Officer, devoted husband, father, and proud grandfather. Born in the monsoon-soaked hills of Kerala, India, his journey from a small village to a purposeful life in the United States is a testimony to God's providence. Marked by seasons of resilience, personal loss, and divine restoration, his story is rooted in the unshakable conviction that God's grace is sufficient in every climb of life.Over the years, Samuel has faithfully served the body of Christ as a Sunday school teacher, mentor, and worship leader. Known for his humble storytelling, deep biblical insight, and unwavering devotion, he has encouraged many to trust the Lord more fully. His heart beats with the truth of 2 Corinthians 12:9-'My grace is sufficient for you, for my power is made perfect in weakness.'Grace in the Climb is his debut memoir, a heartfelt reflection on a life shaped, refined, and sustained by the grace of God.…

Buch. Zustand: Neu. Neuware - In Grace in the Climb, Samuel G. Moolayil shares his deeply personal journey of faith, resilience, and divine grace. From his humble beginnings in the monsoon-soaked hills of Kerala, India, to navigating the challenges of immigration and decades of public service in America, Samuel's story is a testament to God's unwavering faithfulness. Through seasons of loss, including the heartbreaking death of his firstborn son, and moments of triumph, such as the blessings of family and professional milestones, Samuel reflects on how God's grace sustained him through every valley and ascent. ?After the devastating loss of their firstborn son, Samuel and his wife Jessy encountered God in a life-changing way-not in ease, but in surrender. Through that valley of grief came a profound awakening: the redeeming love of Christ, the power of the Holy Spirit, and the steadfast faithfulness of God that carried them forward.Tender, authentic, and brimming with spiritual depth, Grace in the Climb reminds us that valleys are not dead ends but pathways upward-that surrender births strength, and that God's grace is sufficient for every step of the climb.This heartfelt memoir is not just a recounting of life's struggles and victories-it is a spiritual testimony of surrender, hope, and the transformative power of God's love. Grace in the Climb reminds readers that life's hardest climbs are often where grace is most profoundly revealed. ? Samuel G. Moolayil is a retired Chief Financial Officer, devoted husband, father, and proud grandfather. Born in the monsoon-soaked hills of Kerala, India, his journey from a small village to a purposeful life in the United States is a testimony to God's providence. Marked by seasons of resilience, personal loss, and divine restoration, his story is rooted in the unshakable conviction that God's grace is sufficient in every climb of life.Over the years, Samuel has faithfully served the body of Christ as a Sunday school teacher, mentor, and worship leader. Known for his humble storytelling, deep biblical insight, and unwavering devotion, he has encouraged many to trust the Lord more fully. His heart beats with the truth of 2 Corinthians 12:9-'My grace is sufficient for you, for my power is made perfect in weakness.'Grace in the Climb is his debut memoir, a heartfelt reflection on a life shaped, refined, and sustained by the grace of God.…

Taschenbuch. Zustand: Neu. Learn Keras for Deep Neural Networks | A Fast-Track Approach to Modern Deep Learning with Python | Jojo Moolayil | Taschenbuch | xv | Englisch | 2018 | Apress | EAN 9781484242391 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Zustand: New. Second Edition. Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook's Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.What You'll LearnReview machine learning fundamentals such as overfitting, underfitting, and regularization.Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.Apply in-depth linear algebra with PyTorchExplore PyTorch fundamentals andits building blocksWork with tuning and optimizing models Who This Book Is ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.…

Paperback. Zustand: New. Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.What You'll Learn Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks Who This Book Is For Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.…