Название: Machine Learning with Python: Master Pandas, Scikit-learn, and TensorFlow for Building Smart IA Models
Автор: Jоsерh Т. Наndу
Издательство: Independently published
Год: 2024
Страниц: 107
Язык: английский
Формат: pdf, azw3, epub (true), mobi
Размер: 10.1 MB

Machine Learning is revolutionizing the world, and Python is the language of choice for its development. This book equips you with the essential tools - Pandas, Scikit-learn, and TensorFlow - to build and deploy intelligent applications. Written by seasoned practitioners, this book combines clear explanations with practical exercises, helping you gain hands-on experience and build real-world skills. This book, "Machine Learning with Python: Master Pandas, Scikit-learn, and TensorFlow for building Smart AI Models," is your passport to this thrilling world. Through an exciting journey filled with practical exercises, real-world examples, and a dash of humour, you'll not just understand the fundamentals of ML, but also become proficient in using powerful Python libraries like Pandas, Scikit-learn, and TensorFlow to build your own intelligent systems. No prior coding experiences? No worries! We'll walk you through the necessary Python basics, ensuring you have a solid foundation before diving into the heart of ML. So, what are you waiting for? Buckle up, grab your favourite coding buddy (or just grab a cup of coffee!), and get ready to unleash the power of machine learning! In the first chapter, we'll unveil the fascinating world of ML, exploring its various applications and why Python reigns supreme in this domain. We'll also set up your learning environment, so you can start coding right away.

Название: Methodologies, Frameworks, and Applications of Machine Learning
Автор: Рrаmоd Кumаr Srivаstаvа, Аshоk Кumаr Yаdаv
Издательство: IGI Global
Год: 2024
Страниц: 315
Язык: английский
Формат: pdf (true), epub
Размер: 36.4 MB

In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of the latest advancements, theoretical foundations, and practical applications in the realm of Machine Learning. Technology is constantly evolving, and Machine Learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest Machine Learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine Learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. The Chapter 2 focuses on practical implementations of Machine Learning projects using Scikit-learn and TensorFlow libraries in Python. Four distinct projects unfold, each addressing classification, regression, and image classification problems. The step-by-step walkthrough covers model evaluation using classical Machine Learning techniques and deep neural networks.

Название: Python for Artificial Intelligence: A Comprehensive Guide
Автор: Неshаm Моhаmеd Еlshеrif
Издательство: Eldonusa Publishing
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf
Размер: 23.9 MB

Welcome to "Python for Artificial Intelligence: A Comprehensive Guide." In today's rapidly evolving technological landscape, Artificial Intelligence (AI) stands at the forefront of innovation, driving transformative changes across industries and domains. At the heart of AI lies Python, a versatile and powerful programming language renowned for its simplicity, flexibility, and rich ecosystem of libraries and frameworks. This book is crafted as a comprehensive guide to mastering Python for AI, catering to learners of all levels, from aspiring beginners to seasoned practitioners. Whether you're a student, a professional developer, or an AI enthusiast eager to delve into the world of machine learning and deep learning, this book is your roadmap to success. Python boasts a vast ecosystem of libraries and frameworks tailored for AI, including TensorFlow, Keras, PyTorch, scikit-learn, and more. These libraries provide powerful tools and algorithms for building sophisticated AI models with ease.

Название: Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors: Volume 1
Автор: Аtiqur Rаhmаn Аhаd, Sоzо Inоuе, Guillаumе Lореz
Издательство: CRC Press
Год: 2024
Страниц: 457
Язык: английский
Формат: pdf (true)
Размер: 21.9 MB

The book discusses topics such as action recognition, action understanding, gait analysis, gesture recognition, behavior analysis, emotion and affective computing, and related areas. Sensors and cameras are exploited for the analysis and recognition of human activity and behavior. In the book Human Activity and Behavior Analysis: Advances in Computer Vision and Sensors, we have divided across two volumes, 40 wonderful chapters under five parts: Part 1: Healthcare and Emotion (Chapters 1–7), Part 2: Mental Health (Chapters 8–14), Part 3: Nurse Care Records (Chapters 15–26), Part 4: Movement and Sensors (Chapters 27–36), and Part 5: Sports Activity Analysis (Chapters 37–40). In a complex problem such as stress detection, the application of some type of Machine Learning (ML) algorithms makes sense. The vast amount of data in a context where multiple variables, such as HR, HRV, GSR, and ST, might have different outputs based on each other, makes it a prime target for the ML approach. Some of the most common classification algorithms are Support Vector Machines (SVM), K-Nearest Neighbours (K-NN), Random Forests (RF), Decision Trees, and Naive Bayes (NB). The data processing and model development used different Python libraries such as Pandas, Tensorflow, and Keras. To recognize stress from the physiological data, we tested different Machine Learning algorithms. We implemented the approach using Python and the Scikit-learn library.

Название: Analysis and Visualization of Discrete Data Using Neural Networks
Автор: Коji Коуаmаdа
Издательство: World Scientific Publishing
Год: 2024
Страниц: 230
Язык: английский
Формат: pdf (true)
Размер: 53.9 MB

This book serves as a comprehensive step-by-step guide on data analysis and statistical analysis. It covers fundamental operations in Excel, such as table components, formula bar, and ribbon, and introduces visualization techniques and PDE derivation using Excel. It also provides an overview of Google Colab, including code and text cells, and explores visualization and Deep Learning applications. Key features of the book include topics like statistical analysis, regression analysis, optimization, correlation analysis, and neural networks. It adopts a practical approach by providing examples and step-by-step instructions for learners to apply the techniques to real-world problems. The book also highlights the strengths and features of both Excel and Google Colab, allowing learners to leverage the capabilities of each platform. The clear explanations of concepts, visual aids, and code snippets aid comprehension help learners understand the principles of data analysis and statistical analysis. Overall, this book serves as a valuable resource for professionals, researchers, and students seeking to develop skills in data analysis, regression statistics, optimization, and advanced modeling techniques using Excel, Colab, and neural networks. Google Colaboratory (Colab for short) is a free cloud service provided by Google, where you can use the Jupyter notebook and Python to analyze data and build a model for Machine Learning.

Название: An Introduction to Image Classification: From Designed Models to End-to-End Learning
Автор: Кlаus D. Тоеnniеs
Издательство: Springer
Год: 2024
Страниц: 297
Язык: английский
Формат: pdf (true), epub
Размер: 69.6 MB

Image classification is a critical component in computer vision tasks and has numerous applications. Traditional methods for image classification involve feature extraction and classification in feature space. Current state-of-the-art methods utilize end-to-end learning with Deep Neural Networks, where feature extraction and classification are integrated into the model. Understanding traditional image classification is important because many of its design concepts directly correspond to components of a neural network. This knowledge can help demystify the behavior of these networks, which may seem opaque at first sight. The book starts from introducing methods for model-driven feature extraction and classification, including basic Computer Vision techniques for extracting high-level semantics from images. The topic of image classification is presented as a thoroughly curated sequence of steps that gradually increase understanding of the working of a fully trainable classifier. Practical exercises in Python/Keras/Tensorflow have been designed to allow for experimental exploration of these concepts. In each chapter, suitable functions from Python modules are briefly introduced to provide students with the necessary tools to conduct these experiments.

Название: Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python, Second Edition
Автор: Sаntаnu Раttаnауаk
Издательство: Apress
Год: 2023
Страниц: 667
Язык: английский
Формат: pdf (true)
Размер: 15.9 MB

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of Deep Learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of Deep Learning methods, such as autoencoders and variational autoencoders.

Название: Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing
Автор: Сlаudiо Соnti
Издательство: Springer
Год: 2024
Страниц: 393
Язык: английский
Формат: pdf (true)
Размер: 14.6 MB

This book presents a new way of thinking about quantum mechanics and Machine Learning by merging the two. Quantum mechanics and Machine Learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and Machine Learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of Machine Learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs.

Название: Neural Networks for Beginners: Comprehensive Guide to Understanding the Power of Artificial Intelligence
Автор: Sаm Саmрbеll
Издательство: Independently published
Год: 2023
Язык: английский
Формат: pdf
Размер: 24.9 MB

Dive into the foundations of Artificial Intelligence, demystifying the core concept of Neural Networks. From the basics of neurons and synapses to the intricate architecture of these intelligent systems, each chapter unfolds the secrets behind the machines that can learn, adapt, and make decisions on their own. Learn the building blocks of neural networks, from activation functions to the crucial role of data in training these intelligent systems. Gain insights into popular neural network architectures, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), exploring their applications in image recognition, natural language processing, and more. Practicality meets theory as you discover the tools and frameworks powering neural networks, with an introduction to TensorFlow, PyTorch, and the user-friendly Keras. Explore the world of ethical considerations in AI, delving into issues of bias, fairness, and transparency that accompany the development of intelligent systems. Whether you're a student, enthusiast, or professional seeking to grasp the fundamentals, "Neural Networks Unveiled" empowers you with the knowledge to navigate the evolving landscape of Artificial Intelligence. Embark on your journey into the future—where machines learn, adapt, and innovate alongside us. "Neural Networks Unveiled" is your key to unlocking the mysteries of Artificial Intelligence and embracing the limitless possibilities that lie ahead.

Название: Machine Learning Algorithms Using Scikit and TensorFlow Environments
Автор: Рuvvаdi Ваbу Маruthi, Smritу Рrаsаd, Аmit Кumаr Туаgi
Издательство: IGI Global
Год: 2024
Страниц: 473
Язык: английский
Формат: pdf (true)
Размер: 14.2 MB

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. Machine Learning plays a vital role in all major sectors like healthcare, banking, finance, and marketing. There is a need to understand the role and working of ML algorithms in a better way. Google also uses a learning algorithm to rank the web pages whenever we try to browse the internet to get the desired information. Understanding the platform and working of these algorithms is crucial for researchers. In the Chapter 2, the authors have presented an overview of Machine Learning fundamentals and the working of these algorithms with suitable examples. They have also highlighted the importance of major Machine Learning libraries like TensorFlow and SciKit in developing and deploying vast applications. Finally, a case study of ML application is presented to better understand the concept. Future prospects of ML applications are also depicted in detail.