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.

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.

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.

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

Unveiling the power and potential of Generative AI for a limitless future KEY FEATURES ● Holistic and accessible journey into Generative AI. ● Indispensable guide for unleashing Generative AI potential. ● Transforming technology, business, art, and innovation, covering technological advancements and business optimization. DESCRIPTION The Potential of Generative AI invites you for a captivating journey into the revolutionary technology, where machines become co-creators and the line between imagination and reality blurs. You will learn how AI helps doctors, engineers, and scientists solve real-world problems. Next, you will explore use cases where ChatGPT can boost productivity and enhance creativity. The book explores the journey from the origins of this revolutionary technology to its cutting-edge applications. Discover how generative models like GANs and VAEs work, and familiarize yourself with the impact they are making in fields like healthcare, finance, and art. Through real-world case studies and engaging examples, you will witness AI generating life-saving drugs, composing music, and even designing innovative products. This book explores the cutting-edge capabilities and potential of generative AI in the tech landscape. It will help you discover how generative AI can unlock new opportunities and enhance business operations. WHAT YOU WILL LEARN ● Learn about the different types of generative models, how they work, and their impact across various industries including healthcare, finance, and entertainment. ● Explore the creative potential of generative AI in art, music, and design. ● Develop Generative Adversarial Networks (GANs), with a focus on their architecture, training process, and real-world applications. ● Build and deploy generative models, ensuring readers to leverage this powerful technology. ● Perfect the art of generating text, images, music, and even code with AI, utilize your creative potential. WHO THIS BOOK IS FOR This book is for artists, programmers, musicians, designers, writers, researchers, entrepreneurs, scientists, Machine Learning practitioners and dreamers of all sorts. Generative AI awaits and is ready to transform your craft and empower your vision. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative AI in Industries 3. Fundamentals of Generative Models 4. Applications Across Industries 5. Creative Expression with Generative AI 6. Generative AI in Business and Innovation 7. Deep Dive into GANs 8. Building and Deploying Generative Models

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow. Get up and running with TensorFlow, rapidly and painlessly Learn how to use TensorFlow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLP Use extensive abstraction libraries to make development easier and faster Learn how to scale TensorFlow, and use clusters to distribute model training Deploy TensorFlow in a production setting

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.

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Emerald Studies In Finance, Insurance, And Risk Management 7 explores how AI and Automation enhance the basic functions of human resource management.

This book constitutes the revised selected papers of the First International Conference, ICSD 2023, virtually held in Delhi, India, during July 15–16, 2023. The book comprises 31 full papers that were selected from a total of 129 submissions. It provides insights into the latest research and advancements in sustainable development through the integration of machine learning, artificial intelligence, and IoT technologies. It serves as a valuable resource for researchers, practitioners, and policymakers working in the field of sustainable development.