Tag Archives: Foundations

Generative AI & ChatGPT for Beginners Made Useful (2-Books-in-1): Learn Artificial Intelligence Foundations, Master Prompt Engineering and Boost Your Productivity with Conversational AI Tools

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(as of Dec 24,2024 06:01:27 UTC – Details)



Are you interested in diving into the world of Generative AI and ChatGPT, but feeling overwhelmed by the complexities and technical jargon? Look no further! Our 2-Books-in-1 guide is here to help beginners like you understand the fundamentals of Artificial Intelligence and how to effectively use ChatGPT for boosting productivity.

In this comprehensive guide, you will learn the basics of Generative AI, including how it works, its applications, and the ethical considerations surrounding its use. You will also master the art of prompt engineering, a crucial skill for effectively utilizing ChatGPT and other conversational AI tools.

With step-by-step tutorials, practical examples, and hands-on exercises, this book will equip you with the knowledge and skills needed to harness the power of AI in your everyday life. Whether you’re a student, a professional, or just curious about AI, this guide is perfect for anyone looking to demystify the world of Generative AI and ChatGPT.

So why wait? Start your journey into the exciting world of AI today and unlock the potential of conversational AI tools for boosting your productivity and enhancing your work and personal life. Get your copy of “Generative AI & ChatGPT for Beginners Made Useful” now!
#Generative #ChatGPT #Beginners #2Booksin1 #Learn #Artificial #Intelligence #Foundations #Master #Prompt #Engineering #Boost #Productivity #Conversational #Tools

Machine Learning Safety (Artificial Intelligence: Foundations, Theory, and Algorithms)

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Price: $9.48
(as of Dec 24,2024 05:51:38 UTC – Details)




ASIN ‏ : ‎ B0BSRRLX5H
Publisher ‏ : ‎ Springer (April 28, 2023)
Publication date ‏ : ‎ April 28, 2023
Language ‏ : ‎ English
File size ‏ : ‎ 55308 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 432 pages


Machine Learning Safety: Ensuring the Ethical and Responsible Use of Artificial Intelligence

Machine learning and artificial intelligence have the potential to revolutionize various industries and improve countless aspects of our lives. However, with great power comes great responsibility. It is crucial to ensure the safety and ethical use of these technologies to prevent potential harm and negative consequences.

In the book “Artificial Intelligence: Foundations, Theory, and Algorithms,” experts delve into the importance of machine learning safety and provide insights into the key principles and practices that should be followed to promote responsible AI development. From data privacy and security to fairness and transparency, the authors discuss the various aspects of AI safety that must be carefully considered.

One of the key challenges in ensuring machine learning safety is the potential for bias and discrimination in AI algorithms. Biased data sets can lead to unfair outcomes and perpetuate existing inequalities in society. It is essential for developers and researchers to be aware of these issues and take proactive steps to mitigate bias in their models.

Additionally, transparency and accountability are crucial aspects of machine learning safety. Users should have a clear understanding of how AI systems make decisions and be able to hold developers accountable for any errors or biases that may arise. By promoting transparency and open communication, we can build trust in AI technologies and ensure their responsible use.

In conclusion, machine learning safety is a complex and multifaceted issue that requires careful consideration and proactive measures. By following ethical guidelines and implementing best practices, we can harness the transformative power of artificial intelligence while minimizing potential risks and ensuring a more equitable and inclusive future.
#Machine #Learning #Safety #Artificial #Intelligence #Foundations #Theory #Algorithms

Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

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(as of Dec 24,2024 05:31:03 UTC – Details)




Publisher ‏ : ‎ Addison-Wesley Professional; 1st edition (December 5, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 416 pages
ISBN-10 ‏ : ‎ 0135172381
ISBN-13 ‏ : ‎ 978-0135172384
Item Weight ‏ : ‎ 1.2 pounds
Dimensions ‏ : ‎ 6.93 x 0.71 x 9.21 inches

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Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

Are you looking to dive deep into the world of reinforcement learning and understand how to apply it in practice using Python? Look no further than this comprehensive guide that covers the foundations of deep reinforcement learning.

In this book, you will learn the theoretical underpinnings of reinforcement learning, including Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning. You will also explore the basics of deep learning and how it can be integrated with reinforcement learning to create powerful algorithms.

With practical examples and hands-on exercises in Python, you will gain a solid understanding of how to implement deep reinforcement learning algorithms, such as deep Q-networks, policy gradients, and actor-critic methods. By the end of the book, you will be equipped with the knowledge and skills to tackle real-world reinforcement learning problems and build intelligent agents that can learn from their environment.

Whether you are a beginner or an experienced practitioner in machine learning, this book is a must-read for anyone interested in mastering the foundations of deep reinforcement learning. Get your copy today and take your understanding of AI to the next level!
#Foundations #Deep #Reinforcement #Learning #Theory #Practice #Python #AddisonWesley #Data #Analytics #Series

Advances and Open Problems in Federated Learning (Foundations and Trends(r) in Machine Learning)

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(as of Dec 24,2024 05:15:01 UTC – Details)



Advances and Open Problems in Federated Learning (Foundations and Trends® in Machine Learning)

Federated learning has emerged as a promising approach for training machine learning models on distributed data sources while ensuring data privacy and security. In recent years, significant advances have been made in the field, but there are still many open problems that need to be addressed.

One of the key advances in federated learning is the development of more efficient and scalable algorithms. Researchers have proposed novel optimization techniques, such as federated averaging and federated optimization, to reduce communication overhead and improve convergence speed. These advancements have enabled federated learning to be applied to a wide range of applications, including healthcare, finance, and Internet of Things (IoT) devices.

Another important development in federated learning is the integration of differential privacy mechanisms to protect sensitive data during the training process. By adding noise to the gradients or model updates, differential privacy ensures that individual data points cannot be inferred from the trained model. This has paved the way for federated learning to be used in highly regulated industries where data privacy is a top priority.

Despite these advancements, there are still several open problems in federated learning that need to be addressed. One of the main challenges is the heterogeneity of data and computing resources across different devices. Designing algorithms that can handle this heterogeneity while ensuring model performance and convergence is a major research direction in the field.

Another open problem is the robustness of federated learning against adversarial attacks. Since the training process is distributed across multiple devices, attackers can potentially manipulate the training data or model updates to compromise the integrity of the trained model. Developing defense mechanisms against such attacks is a critical area of research in federated learning.

In conclusion, federated learning has made significant strides in recent years, but there are still many challenges that need to be overcome. By addressing these open problems, researchers can continue to advance the field and unlock the full potential of federated learning in real-world applications.
#Advances #Open #Problems #Federated #Learning #Foundations #Trendsr #Machine #Learning

AI Foundations of Neural Networks: Easy To Read Guide Introducing the Foundations Of Neural Networks and AI

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(as of Dec 24,2024 04:49:55 UTC – Details)



Are you curious about the world of artificial intelligence and neural networks, but find the technical jargon overwhelming? Look no further! In this easy-to-read guide, we will introduce you to the foundations of neural networks and AI, breaking down complex concepts into simple terms.

Neural networks are a key component of artificial intelligence, mimicking the way the human brain processes information. They are made up of interconnected nodes, or “neurons,” that work together to process and analyze data. These networks can be trained to recognize patterns, make predictions, and even learn from experience.

The key to understanding neural networks lies in their layers. Each layer consists of multiple neurons that perform specific tasks, such as inputting data, processing information, and producing an output. The connections between neurons are weighted, meaning some connections are stronger than others, influencing the network’s decisions.

Training a neural network involves feeding it data and adjusting the weights of the connections to minimize errors in its predictions. This process, known as backpropagation, allows the network to learn from its mistakes and improve its performance over time.

Artificial intelligence, on the other hand, refers to the broader field of creating intelligent machines that can think and act like humans. Neural networks are just one tool in the AI toolbox, but they have revolutionized fields such as image recognition, natural language processing, and self-driving cars.

By understanding the foundations of neural networks and AI, you can gain insight into how these technologies work and how they are shaping our world. So, whether you’re a beginner looking to dip your toes into the world of AI or a seasoned professional seeking a refresher, this guide is for you. Stay tuned for more in-depth discussions on neural networks and AI in future posts!
#Foundations #Neural #Networks #Easy #Read #Guide #Introducing #Foundations #Neural #Networks

Unsupervised Learning: Foundations of Neural Computation (Paperback or Softback)

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Unsupervised Learning: Foundations of Neural Computation (Paperback or Softback)

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Unsupervised Learning: Foundations of Neural Computation (Paperback or Softback)

Are you looking to dive deep into the world of unsupervised learning and neural computation? Look no further than this comprehensive guide, available in both paperback and softback editions.

This book covers the fundamental principles and techniques of unsupervised learning, a critical aspect of machine learning where the algorithm learns patterns and structures from unlabeled data. With a focus on neural computation, readers will gain a thorough understanding of how neural networks can learn and adapt without explicit supervision.

Whether you are a student, researcher, or practitioner in the field of artificial intelligence and machine learning, this book is a must-have resource. Explore the foundations of neural computation and unlock the potential of unsupervised learning with this invaluable guide. Order your copy today in either paperback or softback format.
#Unsupervised #Learning #Foundations #Neural #Computation #Paperback #Softback

Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media (Artificial Intelligence: Foundations, Theory, and Algorithms)

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Price: $59.99 - $49.49
(as of Dec 24,2024 04:36:56 UTC – Details)




Publisher ‏ : ‎ Springer; 1st ed. 2023 edition (May 24, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 454 pages
ISBN-10 ‏ : ‎ 3031231899
ISBN-13 ‏ : ‎ 978-3031231896
Item Weight ‏ : ‎ 2.31 pounds
Dimensions ‏ : ‎ 6.14 x 1 x 9.21 inches


Foundation Models for Natural Language Processing: Pre-trained Language Models Integrating Media (Artificial Intelligence: Foundations, Theory, and Algorithms)

In recent years, pre-trained language models have revolutionized the field of natural language processing (NLP). These models, such as BERT, GPT-3, and T5, are trained on vast amounts of text data and have shown remarkable performance on a wide range of NLP tasks.

One exciting development in this area is the integration of media, such as images, videos, and audio, into pre-trained language models. By incorporating multimodal information, these models can better understand and generate content that combines text with other forms of media.

This integration of media into pre-trained language models has the potential to significantly enhance the capabilities of NLP systems. For example, a model that can understand both text and images could be used to generate captions for photos, or to answer questions about visual content.

In our upcoming book, “Artificial Intelligence: Foundations, Theory, and Algorithms,” we explore the latest research and developments in pre-trained language models that integrate media. We discuss the challenges and opportunities of multimodal NLP, and provide practical guidance on how to build and deploy these advanced models.

Stay tuned for more updates on this exciting topic, and learn how you can leverage pre-trained language models integrating media to enhance your NLP applications.
#Foundation #Models #Natural #Language #Processing #Pretrained #Language #Models #Integrating #Media #Artificial #Intelligence #Foundations #Theory #Algorithms

Foundations of Service Level Management by

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Foundations of Service Level Management by

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Foundations of Service Level Management

Service Level Management (SLM) is a key component of IT service management that focuses on setting, measuring, and meeting service level agreements (SLAs) between the service provider and the customer. By ensuring that services are delivered in a timely and efficient manner, SLM helps to improve customer satisfaction and maintain the overall quality of service.

There are several foundational elements of Service Level Management that are essential for successful implementation:

1. Understanding customer requirements: The first step in SLM is to understand the requirements and expectations of the customer. This involves identifying key performance indicators (KPIs) and defining SLAs that align with the customer’s business goals.

2. Service catalog: A service catalog provides a detailed list of services offered by the service provider, along with their respective SLAs. This helps to ensure transparency and accountability in service delivery.

3. Performance monitoring: Continuous monitoring of service performance is crucial for identifying and addressing any deviations from SLAs. This may involve the use of monitoring tools and performance reports to track service availability, reliability, and response times.

4. Incident management: When service disruptions occur, it is important to have a robust incident management process in place to quickly resolve issues and minimize downtime. This involves categorizing and prioritizing incidents based on their impact on service levels.

5. Continuous improvement: SLM is an ongoing process that requires regular review and improvement of service levels. By conducting regular service reviews and feedback sessions with customers, service providers can identify areas for improvement and make necessary adjustments to meet changing business needs.

In conclusion, Service Level Management is a critical aspect of IT service delivery that helps to ensure the quality and reliability of services provided to customers. By establishing clear SLAs, monitoring performance, and continuously improving service levels, organizations can enhance customer satisfaction and drive business success.
#Foundations #Service #Level #Management, Managed Services

Explainable AI: Foundations, Methodologies and Applications (Intelligent Syst…

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Explainable AI: Foundations, Methodologies and Applications (Intelligent Syst…

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Explainable AI: Foundations, Methodologies and Applications (Intelligent Systems)

In the rapidly evolving field of artificial intelligence (AI), the concept of explainability has gained significant importance. Explainable AI (XAI) refers to the ability of AI systems to provide clear, understandable explanations for their decisions and actions, enabling users to trust and interpret the results produced by these systems.

Foundations of Explainable AI:
Explainable AI is built on the foundation of transparency and interpretability. This requires AI models to be designed in a way that allows users to understand how the model arrives at its decisions, what features are being considered, and why a particular outcome is predicted. By providing clear explanations, users can better understand and trust the AI system.

Methodologies of Explainable AI:
There are various methodologies and techniques used in XAI to enhance the interpretability of AI models. Some common approaches include feature importance analysis, model-agnostic methods, post-hoc explanations, and interactive visualizations. These techniques aim to provide users with insights into how the AI model works and why it makes certain decisions.

Applications of Explainable AI:
Explainable AI has a wide range of applications across different industries, including healthcare, finance, autonomous vehicles, and more. In healthcare, XAI can help doctors interpret medical images and make more informed decisions. In finance, XAI can provide explanations for credit scoring and investment recommendations. In autonomous vehicles, XAI can help users understand why a particular driving decision was made.

In conclusion, Explainable AI is a crucial aspect of AI development that enhances transparency, trust, and interpretability of AI systems. By leveraging the foundations, methodologies, and applications of XAI, we can build more reliable and ethical AI systems that benefit society as a whole.
#Explainable #Foundations #Methodologies #Applications #Intelligent #Syst..

AI Ethics: A Textbook (Artificial Intelligence: Foundations, Theory, and Algorithms)

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Price: $69.99 - $60.76
(as of Dec 24,2024 02:38:52 UTC – Details)




Publisher ‏ : ‎ Springer; 2023rd edition (March 31, 2023)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 543 pages
ISBN-10 ‏ : ‎ 9811993815
ISBN-13 ‏ : ‎ 978-9811993817
Item Weight ‏ : ‎ 2.06 pounds
Dimensions ‏ : ‎ 6.25 x 1.5 x 9.25 inches


In today’s rapidly advancing world of technology, artificial intelligence (AI) plays a crucial role in shaping the way we live, work, and interact with the world around us. With AI becoming increasingly integrated into our daily lives, it is important to consider the ethical implications of its use.

That’s where “AI Ethics: A Textbook (Artificial Intelligence: Foundations, Theory, and Algorithms)” comes in. This comprehensive textbook delves into the ethical considerations surrounding AI, providing a thorough examination of the principles, theories, and algorithms that underpin this cutting-edge technology.

Written by leading experts in the field, this textbook covers a wide range of topics, including privacy, bias, fairness, accountability, and transparency in AI systems. It also explores the societal impacts of AI, discussing issues such as job displacement, surveillance, and the potential for algorithmic discrimination.

Whether you are a student, researcher, policymaker, or industry professional, “AI Ethics: A Textbook” offers a valuable resource for understanding and navigating the complex ethical challenges posed by artificial intelligence. With its insightful analysis and practical guidance, this textbook is essential reading for anyone interested in the responsible and ethical use of AI.
#Ethics #Textbook #Artificial #Intelligence #Foundations #Theory #Algorithms