Adopting Tensorflow For Real World Ai
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Adopting Tensorflow For Real World Ai
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Author : Naresh R. Jasotani
language : en
Publisher: Naresh R. Jasotani
Release Date :
Adopting Tensorflow For Real World Ai written by Naresh R. Jasotani and has been published by Naresh R. Jasotani this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This book is aimed at providing a practical guidance and approach for utilizing TensorFlow in the real-world based on Python (a programming language). You are not expected to be an expert in Python or know Python at all. The book is intended for newcomers in the field of Machine Learning (ML) and Artificial Intelligence (AI), especially for those, who do not have any statistical background, but they are really interested to learn the details and approach of building a Machine Learning application. This book is also intended for experienced data scientists, Machine Learning engineers, who are generally too focused on building Machine Learning model(s), investing 60-70% of their time in making the model work with a greater level of accuracy, in some cases, they forget the real application and the use case of the application. In most of these cases they end up what we call “overfitting” of the model. The book is expected to focus on developing a Machine Learning application, and in the process detailing multiple real-world practical challenges, steps of a ML application(s). Honestly speaking, the book is meant for “lazy” engineers, aspiring data scientists, Machine Learning engineers, experienced IT professionals in other fields, who like the authors, hate reading through lengthy books with several hundred pages of mathematical models and equations to even getting started with Machine Learning. Many of us are looking for a book with not more than 100-150 pages to gain an understanding on Machine Learning, and it could be an icing on the cake if the book can do away with minimal to no mathematical equations. There are many books, articles, books, guides and documents published on Artificial Intelligence, Machine Learning, and most of them focus on mathematical equations, building models, they tend to be very lengthy spanning several hundred pages. Of-course, they are aimed at serving an exhaustive content for readers to get a deep understanding on the subjects. The aim of this book is not only to just discuss the Machine Learning models, but also focus on explaining the core of Machine Learning with simple examples on regression, classifications, etc. and then discuss a practical approach and steps to build a productionized Machine Learning models with a practical feature engineering. As you read through the book, hopefully the myths of AI and Machine Learning will be debunked, and you will get a very granular/basic to an implementation level understanding and approach of developing ML applications. At the time of writing and conceptualizing this book (in 2019) the authors ensured to keep the content precise, and limit the length of the book in the range of 100-150 pages for those “lazy” but smart engineers out there. After you read this book you can expect to understand the commonly used terminologies of Machine Learning, Artificial Intelligence, learn a little bit of Python enough to be able to write your own ML code, use TensorFlow to build productionized models.
Adopting Tensorflow For Real World Ai
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Author : Deepraj S Chauhan
language : en
Publisher:
Release Date : 2020-05-05
Adopting Tensorflow For Real World Ai written by Deepraj S Chauhan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-05 with categories.
This book is aimed at providing a practical guidance and approach for utilizing TensorFlow in the real-world based on Python (a programming language). You are not expected to be an expert in Python or know Python at all. The book is intended for newcomers in the field of Machine Learning (ML) and Artificial Intelligence (AI), especially for those, who do not have any statistical background, but they are really interested to learn the details and approach of building a Machine Learning application. This book is also intended for experienced data scientists, Machine Learning engineers, who are generally too focused on building Machine Learning model(s), investing 60-70% of their time in making the model work with a greater level of accuracy, in some cases, they forget the real application and the use case of the application. In most of these cases they end up what we call "overfitting" of the model. The book is expected to focus on developing a Machine Learning application, and in the process detailing multiple real-world practical challenges, steps of a ML application(s). Honestly speaking, the book is meant for "lazy" engineers, aspiring data scientists, Machine Learning engineers, experienced IT professionals in other fields, who like the authors, hate reading through lengthy books with several hundred pages of mathematical models and equations to even getting started with Machine Learning. Many of us are looking for a book with not more than 100-150 pages to gain an understanding on Machine Learning, and it could be an icing on the cake if the book can do away with minimal to no mathematical equations. There are many books, articles, books, guides and documents published on Artificial Intelligence, Machine Learning, and most of them focus on mathematical equations, building models, they tend to be very lengthy spanning several hundred pages. Of-course, they are aimed at serving an exhaustive content for readers to get a deep understanding on the subjects. The aim of this book is not only to just discuss the Machine Learning models, but also focus on explaining the core of Machine Learning with simple examples on regression, classifications, etc. and then discuss a practical approach and steps to build a productionized Machine Learning models with a practical feature engineering. As you read through the book, hopefully the myths of AI and Machine Learning will be debunked, and you will get a very granular/basic to an implementation level understanding and approach of developing ML applications. At the time of writing and conceptualizing this book (in 2019) the authors ensured to keep the content precise, and limit the length of the book in the range of 100-150 pages for those "lazy" but smart engineers out there. After you read this book you can expect to understand the commonly used terminologies of Machine Learning, Artificial Intelligence, learn a little bit of Python enough to be able to write your own ML code, use TensorFlow to build productionized models.
Mastering Deep Learning With Tensorflow From Fundamentals To Real World Deployment
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Author : Peter Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-17
Mastering Deep Learning With Tensorflow From Fundamentals To Real World Deployment written by Peter Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-17 with Computers categories.
Explore the realm of artificial intelligence with "Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment." This all-encompassing guide provides an in-depth understanding of AI, machine learning, and deep learning, powered by TensorFlow—Google's leading AI framework. Whether you're a beginner starting your AI journey or a professional looking to elevate your expertise in AI model deployment, this book is tailored to meet your needs. Covering crucial topics like neural network design, convolutional and recurrent neural networks, natural language processing, and computer vision, it offers a robust introduction to TensorFlow and its AI applications. Through hands-on examples and a focus on practical solutions, you'll learn how to apply TensorFlow to solve real-world challenges. From theoretical foundations to deployment techniques, "Mastering Deep Learning with TensorFlow" takes you through every step, preparing you to build, fine-tune, and deploy advanced AI models. By the end, you’ll be ready to harness TensorFlow’s full potential, making strides in the rapidly evolving field of artificial intelligence. This book is an indispensable resource for anyone eager to engage with or advance in AI.
Tensorflow Machine Learning Projects
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Author : Ankit Jain
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30
Tensorflow Machine Learning Projects written by Ankit Jain and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-30 with Computers categories.
Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
Deep Learning With Tensorflow
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Author : Giancarlo Zaccone
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-04-24
Deep Learning With Tensorflow written by Giancarlo Zaccone and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-24 with Computers categories.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
Artificial Intelligence
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Author : Prabhu TL
language : en
Publisher: NestFame Creations Pvt Ltd.
Release Date : 2025-04-05
Artificial Intelligence written by Prabhu TL and has been published by NestFame Creations Pvt Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-05 with Computers categories.
Artificial Intelligence From Fundamentals to the Future – Master the World of Thinking Machines Unlock the secrets behind the most transformative technology of our time. Whether you’re a student, tech enthusiast, entrepreneur, or simply curious about the future, Artificial Intelligence is your ultimate guide to understanding, building, and ethically navigating intelligent systems. This comprehensive, easy-to-follow book takes you on a powerful journey through the core principles, tools, applications, and philosophical challenges of AI—from the basics to the bleeding edge. 🔍 Inside this book, you will discover: ✅ What AI really is—and how it differs from human intelligence ✅ The history, evolution, and types of AI (Narrow, General, and Super Intelligence) ✅ Foundations of machine learning, deep learning, NLP, and computer vision ✅ Real-world AI applications in healthcare, finance, education, marketing, and more ✅ How to build your own AI models with hands-on examples ✅ Emerging technologies: quantum AI, emotional intelligence, and AGI ✅ Ethics, bias, consciousness, and the role of AI in reshaping humanity 👩💻 Who is this book for? Students & professionals looking to upskill in AI Entrepreneurs & product creators wanting to leverage AI Academics & researchers exploring the cutting edge Policy makers & thinkers interested in ethical implications Anyone curious about how AI is shaping our present—and future 🌍 More than a book—it’s a roadmap for the intelligent age. In a world increasingly shaped by algorithms, this book empowers you to not just understand AI—but to use it wisely, build it responsibly, and shape its future with intention and impact. Start your journey today. The future isn’t just coming— AI is already here. Are you ready?
Machine Learning And Deep Learning In Real Time Applications
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Author : Paawan Sharma
language : en
Publisher: Engineering Science Reference
Release Date : 2020
Machine Learning And Deep Learning In Real Time Applications written by Paawan Sharma and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.
Adopting Ai For Business Transformation
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Author : Andrea Marchiotto
language : en
Publisher: BPB Publications
Release Date : 2024-11-26
Adopting Ai For Business Transformation written by Andrea Marchiotto and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-26 with Computers categories.
DESCRIPTION Adopting AI for Business Transformation offers a comprehensive guide for businesses ready to transition into AI and automation and transform their operations and ways of working. The book focuses on various industries providing practical insights and step-by-step guides for AI adoption and integration. Through real-world examples, expert insights, and case studies, the author shares his experience in product management, e-commerce, and digital platforms, presenting strategies for embracing AI and overcoming implementation challenges. Learn the key principles of AI adoption, get guidance on generative AI and large language models, and how to best leverage their power with advanced prompt engineering, organizational structures, workforce education and upskilling, cloud-based business models, and ready-to-use AI frameworks that have successfully transformed traditional businesses into AI-driven, modern, efficient business models. By the end of this book, you will understand how to adopt and integrate AI, empowering your team to leverage AI in your business operations, reducing the time spent on operational tasks, and unlocking new growth opportunities. By adopting the strategies in this book, you will reduce operational costs, improve efficiency, and future-proof your business. KEY FEATURES ● Introduction to AI concepts, types of AI, and real-world examples. ● Overview of popular frameworks, strengths and weaknesses, and selection criteria. ● Use cases of generative AI for innovation across diverse industries. WHAT YOU WILL LEARN ● Grasp how AI is revolutionizing business operations and driving innovation. ● Understand AI adoption frameworks and learn how to choose the right one for your organization. ● Explore AI applications in healthcare, finance, and manufacturing, from patient care to fraud detection. ● Discover the role of AI in Web 3.0 and its intersection with blockchain. ● Learn how AI enhances customer service, education, agriculture, and retail, transforming industry landscapes. WHO THIS BOOK IS FOR This book is ideal for CXOs, decision-makers, entrepreneurs, business owners and leaders, consultants, and digital transformation advisors interested in the democratization of AI, generative AI, and large language models. TABLE OF CONTENTS 1. The Power of AI in Modern Businesses 2. AI Adoption Frameworks for Business Leaders and Entrepreneurs 3. AI Adoption Frameworks for Developers 4. Building an AI-ready Culture 5. Practical Applications of Generative AI and Large Language Models 6. AI in Emerging Technologies 7. Latest Developments and Breakthroughs in Artificial Intelligence
Artificial Intelligence Algorithms Using Python
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Author : Dr Gauri M. Dhopavkar
language : en
Publisher: RK Publication
Release Date : 2024-12-31
Artificial Intelligence Algorithms Using Python written by Dr Gauri M. Dhopavkar and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-31 with Computers categories.
Artificial Intelligence Algorithms Using Python the fundamentals and advanced concepts of AI algorithms through practical Python implementations. Covering machine learning, deep learning, natural language processing, and reinforcement learning, this provides a hands-on approach to building intelligent systems. It delves into algorithm design, optimization techniques, and real-world applications, making it ideal for students, researchers, and professionals. With a strong focus on code-driven learning, it enables readers to develop AI models efficiently using Python libraries such as Tensor Flow, scikit -learn, and PyTorch, bridging the gap between theoretical concepts and practical implementation.
Machine Learning Algorithms Using Scikit And Tensorflow Environments
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Author : Baby Maruthi, Puvvadi
language : en
Publisher: IGI Global
Release Date : 2023-12-18
Machine Learning Algorithms Using Scikit And Tensorflow Environments written by Baby Maruthi, Puvvadi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-18 with Computers categories.
Machine learning is able to solve real-time problems. It has several algorithms such as classification, clustering, and more. To learn these essential algorithms, we require tools like Scikit and TensorFlow. Machine Learning Algorithms Using Scikit and TensorFlow Environments assists researchers in learning and implementing these critical algorithms. Covering key topics such as classification, artificial neural networks, prediction, random forest, and regression analysis, this premier reference source is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students.