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Mastering Deep Learning: A Comprehensive Guide to Master Deep Learning

Автор: Limpopo5 от 2023-12-04, 18:19:44
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Mastering Deep Learning: A Comprehensive Guide to Master Deep LearningНазвание: Mastering Deep Learning: A Comprehensive Guide to Master Deep Learning
Автор: Кris Неrmаns, Суbеllium Ltd
Издательство: Cybellium Ltd
Год: 2023
Страниц: 594
Язык: английский
Формат: pdf
Размер: 43.9 MB

Unleash the Power of Neural Networks for Intelligent Solutions. In the landscape of Artificial Intelligence and Machine Learning, Deep Learning stands as a revolutionary force that is shaping the future of technology. "Mastering Deep Learning" is your ultimate guide to comprehending and harnessing the potential of deep neural networks, empowering you to create intelligent solutions that drive innovation. As the capabilities of technology expand, Deep Learning emerges as a transformative approach that unlocks the potential of Artificial Intelligence. "Mastering Deep Learning" offers a comprehensive exploration of this cutting-edge field—an indispensable toolkit for data scientists, engineers, and enthusiasts. This book caters to both beginners and experienced learners aiming to excel in deep learning concepts, algorithms, and applications. In the landscape of Artificial Intelligence, Deep Learning is reshaping technology and innovation. "Mastering Deep Learning" equips you with the knowledge needed to leverage deep neural networks, enabling you to create intelligent solutions that push the boundaries of possibilities. Whether you're a seasoned practitioner or new to the world of Deep Learning, this book will guide you in building a solid foundation for effective AI-driven solutions. Your journey to mastering Deep Learning starts here.

Machine Learning for Causal Inference

Автор: Limpopo5 от 2023-12-01, 18:25:58
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Machine Learning for Causal InferenceНазвание: Machine Learning for Causal Inference
Автор: Shеng Li, Zhiхuаn Сhu
Издательство: Springer
Год: 2023
Страниц: 302
Язык: английский
Формат: pdf (true), epub
Размер: 26.2 MB

This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how Machine Learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.

Обучение машины распознаванию образов

Автор: natagu от 2023-11-30, 12:07:57
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Обучение машины распознаванию образов
Название: Обучение машины распознаванию образов
Автор: Аркадьев А.Г., Браверман Э.М.
Издательство: М.: Наука
Год: 1964
Язык: Русский
Формат: djvu
Размер: 14,6 Мб
Качество: хорошее, текстовый слой, оглавление.
Кол-во страниц: 112

Описание: Объективность свойства образов, его независимость от индивидуальных особенностей обучающихся позволяет, например, людям, учившимся в разных школах, у разных учителей, успешно читать один и тот же ранее не известный им почерк. Однако, обладая указанным объективным свойством, сами образы в то же время до некоторой степени расплывчаты, так как вопрос о том, принадлежит объект к данному образу или нет, не всегда может быть решен однозначно.

Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications

Автор: Limpopo5 от 2023-11-23, 18:13:36
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Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applicationsНазвание: Explainable Artificial Intelligence (XAI): Concepts, enabling tools, technologies and applications
Автор: Реthuru Rаj, Utku Коsе, Ushа Sаkthivеl, Susilа Nаgаrаjаn
Издательство: The Institution of Engineering and Technology
Год: 2023
Страниц: 530
Язык: английский
Формат: pdf (true)
Размер: 30.0 MB

The world is keen to leverage multi-faceted AI techniques and tools to deploy and deliver the next generation of business and IT applications. Resource-intensive gadgets, machines, instruments, appliances, and equipment spread across a variety of environments are empowered with AI competencies. Connected products are collectively or individually enabled to be intelligent in their operations, offering and output. AI is being touted as the next-generation technology to visualize and realize a bevy of intelligent systems, networks and environments. However, there are challenges associated with the huge adoption of AI methods. As we give full control to AI systems, we need to know how these AI models reach their decisions. Trust and transparency of AI systems are being seen as a critical challenge. Building knowledge graphs and linking them with AI systems are being recommended as a viable solution for overcoming this trust issue and the way forward to fulfil the ideals of explainable AI. Explainable Artificial Intelligence (XAI) is a set of promising algorithms and approaches that empower users to comprehend why and how AI models reach a particular decision. These AI models must be penetrative, pervasive and persuasive, trustworthy, and transparent. XAI plays a significant role in ensuring a heightened confidence in the recommendations made by AI systems.

Handbook on Federated Learning: Advances, Applications and Opportunities

Автор: Limpopo5 от 2023-11-23, 14:02:11
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Handbook on Federated Learning: Advances, Applications and OpportunitiesНазвание: Handbook on Federated Learning: Advances, Applications and Opportunities
Автор: Sаrаvаnаn Кrishnаn, А. Jоsе Аnаnd, R. Srinivаsаn, R. Каvithа, S. Surеsh
Издательство: CRC Press
Год: 2024
Страниц: 363
Язык: английский
Формат: pdf (true), epub
Размер: 28.4 MB

Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated Learning has developed as a model of education in these situations. Federated Learning (FL) is an expert form of decentralized Machine Learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of Artificial Intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional Machine Learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional Machine Learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation.

Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications

Автор: Limpopo5 от 2023-11-16, 21:24:46
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Generative AI on AWS: Building Context-Aware Multimodal Reasoning ApplicationsНазвание: Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications
Автор: Сhris Frеglу, Аntjе Ваrth, Shеlbее Еigеnbrоdе
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 309
Язык: английский
Формат: epub (true)
Размер: 42.1 MB

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. A basic understanding of Python and deep learning frameworks such as TensorFlow or PyTorch should be sufficient to understand the code samples used throughout the book. Familiarity with AWS is not required to learn the concepts, but it is useful for some of the AWS-specific samples.

What Are AI Agents?

Автор: Limpopo5 от 2023-11-16, 16:59:34
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What Are AI Agents?Название: What Are AI Agents? When and How to Use LLM Agents
Автор: Веnjаmin Lаbаsсhin
Издательство: O’Reilly Media, Inc.
Год: 2024
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

AI agents represent the latest milestone in humanity's computational toolbox. Powered by large language models (LLMs) and the data they were trained on, AI agents are tools that let you interact with specialized LLMs to achieve more productive or creative workflows with less technical hassle. The last century in the history of computing has had many notable milestones: the invention of the computer; the development of the personal computer as we know it; the internet; the smart phone; machine learning; and cloud computing. Every few decades it seems societies are thrust forward, riding the wave of some new computational innovation. If you are reading this, then congratulations (or I’m sorry?), you are living during another one of these milestones in computational advancement. Code assistant agents are AI agents that are fueled by models specifically designed to help users like you write code more productively and efficiently. Popular code assistant agents include GitHub Copilot, Amazon CodeWhisperer, and Hugging Face’s StarCoder. Code assistant agents are tools designed to edit error-ridden code, autocomplete simple functions to common coding problems, or design templates for more difficult coding problems.

Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using Tensorflow and Keras

Автор: Limpopo5 от 2023-11-15, 22:31:10
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Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using Tensorflow and KerasНазвание: Neural Networks with Python: Design CNNs, Transformers, GANs and capsule networks using Tensorflow and Keras
Автор: Меi Wоng
Издательство: GitforGits
Год: November 2023
Страниц: 253
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of Machine Learning. In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios.

Python for Machine Learning: From Fundamentals to Real-World Applications

Автор: Limpopo5 от 2023-11-14, 15:26:15
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Python for Machine Learning: From Fundamentals to Real-World ApplicationsНазвание: Python for Machine Learning: From Fundamentals to Real-World Applications
Автор: Каmеrоn Нussаin, Frаhааn Нussаin
Издательство: Sonar Publishing
Год: November 10, 2023
Страниц: 362
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.2 MB

"Python for Machine Learning: From Fundamentals to Real-World Applications" is your comprehensive roadmap to mastering Machine Learning with Python. Whether you're a beginner looking to enter the exciting world of Data Science or an experienced programmer aiming to delve deeper into Machine Learning, this book provides you with the knowledge and practical skills needed to excel in the field. Starting with the fundamentals, you'll learn the essential concepts of Machine Learning and get a solid grasp of Python programming. As you progress, you'll explore the core Machine Learning algorithms, data preprocessing techniques, and Python libraries that are crucial for building predictive models. From linear regression and decision trees to neural networks and Deep Learning, this book covers a wide range of Machine Learning topics. Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn and make predictions or decisions without being explicitly programmed.

AI Applications to Communications and Information Technologies: The Role of Ultra Deep Neural Networks

Автор: Limpopo5 от 2023-11-13, 19:57:51
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AI Applications to Communications and Information Technologies: The Role of Ultra Deep Neural NetworksНазвание: AI Applications to Communications and Information Technologies: The Role of Ultra Deep Neural Networks
Автор: Dаniеl Мinоli, Веnеdiсt Оссhiоgrоssо
Издательство: Wiley-IEEE Press
Год: 2024
Страниц: 493
Язык: английский
Формат: pdf (true), epub
Размер: 26.4 MB

Apply the technology of the future to networking and communications. Artificial Intelligence (AI), which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks (ANNs), large language models, and Deep Learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. Artificial Intelligence (AI) is a subfield of Computer Science (CS) that focuses on the creation of computer-based systems, applications, and algorithms that mimic, to the degree possible, some cognitive processes intrinsic to human intelligence. The field has had a long history and is now blossoming in an all-encompassing manner. AI technologies, particularly Machine Learning (ML) and Deep Learning (DL), are becoming ubiquitous in nearly all aspects of modern life. DL is a subfield of ML as discussed below. The goals of learning are (i) understanding a process or phenomenon and (ii) making prediction about outcomes, namely, inferring a function or relationship that maps the input to an output in such a manner that the learned relationship can be used to predict the future output from a future input. AI applications, and ML/DL- based systems in particular, are positioned to take over complex tasks generally performed by humans (decision- makers) or to provide added support to people. Siri, Alexa, augmented reality (AR), autonomous driving, and object recognition are just a few examples of AI applications.

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