<![CDATA[MKLab - FREE EBOOKS]]> https://mklab.gr/ Mon, 14 Sep 2026 16:32:03 +0000 MyBB <![CDATA[Natural Language Processing and Large Language Models [Zeng]]]> https://mklab.gr/showthread.php?tid=1749 Sun, 30 Aug 2026 19:06:14 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1749 Authors: Chengqing Zong, Yang Zhao, Yanjun Ma
Publication date: 24 July 2026 (eBook); 25 July 2026 (hardcover)
Publisher: Springer Singapore
Edition: 1st edition
Length: XIV + 394 pages

Quote:
The book is available as free pdf access


Summary

This book provides a broad introduction to Natural Language Processing (NLP) that deliberately combines mathematical and conceptual foundations with executable code and practical case studies. It begins with the building blocks of modern NLP—neural networks, distributed representations, sequence-generation models, and basic language models—and then develops the ideas behind pre-trained language models and large language models. Important architectures and techniques include Transformers, BERT, GPT, ERNIE, and reinforcement learning from human feedback (RLHF). The emphasis is not simply on describing these models, but on explaining how they are trained, adapted, and applied. The authors provide executable Jupyter notebooks and datasets that can be run through Baidu AI Studio. 

The second half of the book is strongly application-oriented. Separate chapters cover tokenization and word segmentation, sentiment classification, information extraction, semantic text matching, PEGASUS-based text summarization, intent recognition with ERNIE 3.0, machine reading comprehension, machine translation, and question answering with LLMs. The progression therefore moves from fundamental neural representations to increasingly sophisticated language tasks. In particular, the book shows how modern pre-trained models can be fine-tuned for specific NLP problems rather than treating an LLM as a mysterious black box. 

The text is aimed mainly at senior undergraduate and graduate students in computer science or AI, developers, researchers, and newcomers to NLP. Its main strength is the balance between theory and implementation: readers learn both why techniques such as attention, Transformers, BERT and GPT work and how to build systems using them. At nearly 400 pages, it is substantial enough to serve as an NLP course text, while its case-study structure makes it useful for self-study or practical AI projects. It is particularly valuable for someone who wants to progress from classical NLP toward contemporary LLM-based systems without jumping directly into high-level APIs. 

Key takeaways
  • Theory + practice: combines NLP foundations with runnable implementations rather than treating them separately.
  • Modern coverage: follows the development from neural networks and embeddings through Transformer → BERT/GPT → modern LLM applications. 
  • Application-heavy: includes real implementations of summarization, sentiment analysis, information extraction, question answering, machine translation, semantic matching, and related tasks. 
  • Good learning path: particularly suitable for readers who know some Python/machine learning and want a systematic introduction to NLP and LLM engineering.
  • Major advantage: the entire book is Open Access, including downloadable PDF and EPUB editions. 


Springer book page and free full text]]> Authors: Chengqing Zong, Yang Zhao, Yanjun Ma
Publication date: 24 July 2026 (eBook); 25 July 2026 (hardcover)
Publisher: Springer Singapore
Edition: 1st edition
Length: XIV + 394 pages

Quote:
The book is available as free pdf access


Summary

This book provides a broad introduction to Natural Language Processing (NLP) that deliberately combines mathematical and conceptual foundations with executable code and practical case studies. It begins with the building blocks of modern NLP—neural networks, distributed representations, sequence-generation models, and basic language models—and then develops the ideas behind pre-trained language models and large language models. Important architectures and techniques include Transformers, BERT, GPT, ERNIE, and reinforcement learning from human feedback (RLHF). The emphasis is not simply on describing these models, but on explaining how they are trained, adapted, and applied. The authors provide executable Jupyter notebooks and datasets that can be run through Baidu AI Studio. 

The second half of the book is strongly application-oriented. Separate chapters cover tokenization and word segmentation, sentiment classification, information extraction, semantic text matching, PEGASUS-based text summarization, intent recognition with ERNIE 3.0, machine reading comprehension, machine translation, and question answering with LLMs. The progression therefore moves from fundamental neural representations to increasingly sophisticated language tasks. In particular, the book shows how modern pre-trained models can be fine-tuned for specific NLP problems rather than treating an LLM as a mysterious black box. 

The text is aimed mainly at senior undergraduate and graduate students in computer science or AI, developers, researchers, and newcomers to NLP. Its main strength is the balance between theory and implementation: readers learn both why techniques such as attention, Transformers, BERT and GPT work and how to build systems using them. At nearly 400 pages, it is substantial enough to serve as an NLP course text, while its case-study structure makes it useful for self-study or practical AI projects. It is particularly valuable for someone who wants to progress from classical NLP toward contemporary LLM-based systems without jumping directly into high-level APIs. 

Key takeaways
  • Theory + practice: combines NLP foundations with runnable implementations rather than treating them separately.
  • Modern coverage: follows the development from neural networks and embeddings through Transformer → BERT/GPT → modern LLM applications. 
  • Application-heavy: includes real implementations of summarization, sentiment analysis, information extraction, question answering, machine translation, semantic matching, and related tasks. 
  • Good learning path: particularly suitable for readers who know some Python/machine learning and want a systematic introduction to NLP and LLM engineering.
  • Major advantage: the entire book is Open Access, including downloadable PDF and EPUB editions. 


Springer book page and free full text]]> <![CDATA[Mathematical Foundations of Deep Learning [Ye]]]> https://mklab.gr/showthread.php?tid=1712 Thu, 20 Aug 2026 19:26:42 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1712 Author: Xiaojing Ye
arXiv: 2603.18387
Submitted: 19 March 2026
Type: Draft book; final version published by Chapman & Hall/CRC in 2026. 

This work develops a rigorous mathematical framework for understanding modern deep learning. Rather than treating neural networks mainly as engineering tools, Ye presents them as mathematical objects: neural networks are viewed as classes of function approximators, while training becomes a large-scale, generally non-convex optimization problem. The book connects deep learning with approximation theory, functional analysis, probability, statistics, optimization and dynamical systems, with particular emphasis on explaining why neural networks can approximate complex functions, how they are trained, and what theoretical principles govern their behavior. A major early result discussed is the Universal Approximation Theorem, followed by network architectures, activation functions, automatic differentiation and deterministic and stochastic optimization methods. 

A distinctive feature is that the mathematical treatment extends well beyond standard supervised neural networks. Deep networks are linked to optimal control theory, including Euler–Lagrange equations, Hamiltonian systems, the Pontryagin Maximum Principle and Hamilton–Jacobi–Bellman equations. This leads naturally to Neural ODEs and then to reinforcement learning, where Markov decision processes, Bellman equations and policy-improvement methods are developed within essentially the same mathematical framework. The final part turns to modern generative AI, covering variational autoencoders, GANs, diffusion models and flow matching, together with their interpretation through probability distributions, stochastic differential equations and probability-density control. 

The central message is that seemingly different areas of modern AI share a surprisingly unified mathematical structure. Approximation explains what neural networks can represent; optimization explains how their parameters are learned; control theory and dynamic programming explain sequential decision-making; and probability, differential equations and transport ideas explain many modern generative models. The intended audience is advanced undergraduate or graduate students and researchers with a solid background in calculus, linear algebra and probability, with real analysis being useful. The work is therefore particularly valuable as a bridge between traditional mathematics and contemporary AI, rather than as a practical programming manual. 

Key takeaways
  • Deep learning is fundamentally a mathematical subject, drawing heavily on approximation theory, optimization, probability and dynamical systems.
  • Neural-network training can be understood as solving a high-dimensional optimization problem, while deep architectures can also be interpreted as discretized dynamical or control systems.
  • Reinforcement learning and optimal control are closely connected through Bellman and Hamilton–Jacobi–Bellman equations.
  • Modern generative methods—including diffusion models and flow matching—fit naturally into a framework based on probability distributions, stochastic differential equations and continuous-time dynamics. 


arXiv paper]]> Author: Xiaojing Ye
arXiv: 2603.18387
Submitted: 19 March 2026
Type: Draft book; final version published by Chapman & Hall/CRC in 2026. 

This work develops a rigorous mathematical framework for understanding modern deep learning. Rather than treating neural networks mainly as engineering tools, Ye presents them as mathematical objects: neural networks are viewed as classes of function approximators, while training becomes a large-scale, generally non-convex optimization problem. The book connects deep learning with approximation theory, functional analysis, probability, statistics, optimization and dynamical systems, with particular emphasis on explaining why neural networks can approximate complex functions, how they are trained, and what theoretical principles govern their behavior. A major early result discussed is the Universal Approximation Theorem, followed by network architectures, activation functions, automatic differentiation and deterministic and stochastic optimization methods. 

A distinctive feature is that the mathematical treatment extends well beyond standard supervised neural networks. Deep networks are linked to optimal control theory, including Euler–Lagrange equations, Hamiltonian systems, the Pontryagin Maximum Principle and Hamilton–Jacobi–Bellman equations. This leads naturally to Neural ODEs and then to reinforcement learning, where Markov decision processes, Bellman equations and policy-improvement methods are developed within essentially the same mathematical framework. The final part turns to modern generative AI, covering variational autoencoders, GANs, diffusion models and flow matching, together with their interpretation through probability distributions, stochastic differential equations and probability-density control. 

The central message is that seemingly different areas of modern AI share a surprisingly unified mathematical structure. Approximation explains what neural networks can represent; optimization explains how their parameters are learned; control theory and dynamic programming explain sequential decision-making; and probability, differential equations and transport ideas explain many modern generative models. The intended audience is advanced undergraduate or graduate students and researchers with a solid background in calculus, linear algebra and probability, with real analysis being useful. The work is therefore particularly valuable as a bridge between traditional mathematics and contemporary AI, rather than as a practical programming manual. 

Key takeaways
  • Deep learning is fundamentally a mathematical subject, drawing heavily on approximation theory, optimization, probability and dynamical systems.
  • Neural-network training can be understood as solving a high-dimensional optimization problem, while deep architectures can also be interpreted as discretized dynamical or control systems.
  • Reinforcement learning and optimal control are closely connected through Bellman and Hamilton–Jacobi–Bellman equations.
  • Modern generative methods—including diffusion models and flow matching—fit naturally into a framework based on probability distributions, stochastic differential equations and continuous-time dynamics. 


arXiv paper]]> <![CDATA[Understanding Large Language Models [Kunz]]]> https://mklab.gr/showthread.php?tid=1602 Sat, 15 Aug 2026 22:24:41 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1602 Understanding Large Language Models
BY  Jenny Kunz

Summary

This pdf file contains Jenny Kunz's 2024 doctoral dissertation from Linköping University, titled "Understanding Large Language Models: Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation". 

The research explores methods for evaluating and understanding the inner workings and outputs of large language models (LLMs), focusing on two primary areas: model interpretability via probing classifiers to analyze intermediate layer representations, and model explainability by evaluating the quality, properties, and alignment of natural language explanations generated by self-rationalizing models.


BOOK [PDF]]]> Understanding Large Language Models
BY  Jenny Kunz

Summary

This pdf file contains Jenny Kunz's 2024 doctoral dissertation from Linköping University, titled "Understanding Large Language Models: Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation". 

The research explores methods for evaluating and understanding the inner workings and outputs of large language models (LLMs), focusing on two primary areas: model interpretability via probing classifiers to analyze intermediate layer representations, and model explainability by evaluating the quality, properties, and alignment of natural language explanations generated by self-rationalizing models.


BOOK [PDF]]]> <![CDATA[Understanding Agentic AI]]> https://mklab.gr/showthread.php?tid=1597 Sat, 15 Aug 2026 19:36:02 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1597
Summary

KDnuggets has released a free eBook, Understanding Agentic AI: An Executive Briefing, aimed primarily at CEOs, CTOs, CIOs, and other technology leaders who want a practical understanding of AI agents without getting lost in rapidly changing frameworks. The book addresses the confusion surrounding the term “agent,” distinguishing genuine agentic systems from simple chatbots or scripts, and focuses on the fundamental architecture that remains consistent across different technologies. 

It explains how an AI agent operates through loops, interacts with external tools and APIs, maintains memory and context, and where such systems are most likely to fail. Rather than teaching a particular framework, the briefing provides a conceptual foundation that helps readers evaluate commercial and open-source agentic AI systems, assess vendors, and make better strategic decisions about adopting the technology. The eBook is available free to KDnuggets subscribers. 

EBOOK]]>
Summary

KDnuggets has released a free eBook, Understanding Agentic AI: An Executive Briefing, aimed primarily at CEOs, CTOs, CIOs, and other technology leaders who want a practical understanding of AI agents without getting lost in rapidly changing frameworks. The book addresses the confusion surrounding the term “agent,” distinguishing genuine agentic systems from simple chatbots or scripts, and focuses on the fundamental architecture that remains consistent across different technologies. 

It explains how an AI agent operates through loops, interacts with external tools and APIs, maintains memory and context, and where such systems are most likely to fail. Rather than teaching a particular framework, the briefing provides a conceptual foundation that helps readers evaluate commercial and open-source agentic AI systems, assess vendors, and make better strategic decisions about adopting the technology. The eBook is available free to KDnuggets subscribers. 

EBOOK]]> <![CDATA[Unlocking Artificial Intelligence [Mutschler]]]> https://mklab.gr/showthread.php?tid=1583 Wed, 12 Aug 2026 17:58:50 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1583 Unlocking Artificial Intelligence 
BY Christopher Mutschler

Summary

Unlocking Artificial Intelligence: From Theory to Applications is an open-access 2024 Springer book that provides a broad overview of modern artificial intelligence and machine learning, connecting theoretical methods with practical applications. 
It covers topics such as automated machine learning, sequence-based and deep learning, reinforcement learning, learning with limited data, uncertainty and trustworthy AI, process-aware learning, optimization, and semantic understanding, before demonstrating their use in autonomous systems, medical imaging, wireless positioning, energy networks, logistics, transportation, industrial monitoring, and IoT. 
Overall, the book emphasizes how recent AI research can be translated into reliable, practical systems and highlights both the opportunities and challenges of deploying AI in real-world environments. 

BOOK [FREE AS A PDF]]]> Unlocking Artificial Intelligence 
BY Christopher Mutschler

Summary

Unlocking Artificial Intelligence: From Theory to Applications is an open-access 2024 Springer book that provides a broad overview of modern artificial intelligence and machine learning, connecting theoretical methods with practical applications. 
It covers topics such as automated machine learning, sequence-based and deep learning, reinforcement learning, learning with limited data, uncertainty and trustworthy AI, process-aware learning, optimization, and semantic understanding, before demonstrating their use in autonomous systems, medical imaging, wireless positioning, energy networks, logistics, transportation, industrial monitoring, and IoT. 
Overall, the book emphasizes how recent AI research can be translated into reliable, practical systems and highlights both the opportunities and challenges of deploying AI in real-world environments. 

BOOK [FREE AS A PDF]]]> <![CDATA[Automated Machine Learning [Hutter]]]> https://mklab.gr/showthread.php?tid=1582 Wed, 12 Aug 2026 17:55:59 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1582 Automated Machine Learning 
BY Frank Hutter

Summary

Automated Machine Learning: Methods, Systems, Challenges (2019), edited by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, provides a comprehensive introduction to Automated Machine Learning (AutoML), which aims to reduce the need for human experts to manually select machine-learning algorithms, architectures, preprocessing methods, and hyperparameters.

 The book explains key AutoML techniques such as hyperparameter optimization, meta-learning, and neural architecture search, and examines practical systems including Auto-WEKA, Hyperopt-Sklearn, auto-sklearn, TPOT, and the Automatic Statistician. It also reviews the results of international AutoML competitions, highlighting the strengths and limitations of different approaches. Overall, the book presents AutoML as a way to make machine learning more accessible, efficient, and increasingly automated for researchers, students, and practitioners. 

BOOK [FREE AS A PDF]]]> Automated Machine Learning 
BY Frank Hutter

Summary

Automated Machine Learning: Methods, Systems, Challenges (2019), edited by Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren, provides a comprehensive introduction to Automated Machine Learning (AutoML), which aims to reduce the need for human experts to manually select machine-learning algorithms, architectures, preprocessing methods, and hyperparameters.

 The book explains key AutoML techniques such as hyperparameter optimization, meta-learning, and neural architecture search, and examines practical systems including Auto-WEKA, Hyperopt-Sklearn, auto-sklearn, TPOT, and the Automatic Statistician. It also reviews the results of international AutoML competitions, highlighting the strengths and limitations of different approaches. Overall, the book presents AutoML as a way to make machine learning more accessible, efficient, and increasingly automated for researchers, students, and practitioners. 

BOOK [FREE AS A PDF]]]> <![CDATA[Mathematics of Data Science [Bandeira]]]> https://mklab.gr/showthread.php?tid=1151 Thu, 16 Jul 2026 13:45:50 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1151 Mathematics of Data Science 
by Afonso S. Bandeira

Summary

Mathematics of Data Science offers a broad introduction to the mathematical ideas that power modern data science and machine learning. Rather than focusing on programming tools or software, the book explains the core mathematical principles that allow algorithms to analyze, interpret, and learn from data. It begins by exploring the opportunities and challenges of working with high-dimensional data before covering essential topics such as principal component analysis, linear regression, optimization, classification, graph theory, clustering, and dimensionality reduction. 

The authors also introduce the mathematical foundations of deep learning, along with advanced subjects including concentration inequalities, graph Laplacians, compressive sensing, sparsity, and low-rank matrix recovery. Throughout the book, the emphasis is on developing intuition as well as rigorous understanding, showing how ideas from linear algebra, probability, optimization, and statistics come together to solve real-world data problems. By connecting theory with practical applications, the text provides readers with a solid foundation for understanding why modern data science methods work and how they can be applied effectively across a wide range of disciplines. 


BOOK [PDF]]]> Mathematics of Data Science 
by Afonso S. Bandeira

Summary

Mathematics of Data Science offers a broad introduction to the mathematical ideas that power modern data science and machine learning. Rather than focusing on programming tools or software, the book explains the core mathematical principles that allow algorithms to analyze, interpret, and learn from data. It begins by exploring the opportunities and challenges of working with high-dimensional data before covering essential topics such as principal component analysis, linear regression, optimization, classification, graph theory, clustering, and dimensionality reduction. 

The authors also introduce the mathematical foundations of deep learning, along with advanced subjects including concentration inequalities, graph Laplacians, compressive sensing, sparsity, and low-rank matrix recovery. Throughout the book, the emphasis is on developing intuition as well as rigorous understanding, showing how ideas from linear algebra, probability, optimization, and statistics come together to solve real-world data problems. By connecting theory with practical applications, the text provides readers with a solid foundation for understanding why modern data science methods work and how they can be applied effectively across a wide range of disciplines. 


BOOK [PDF]]]> <![CDATA[Machine Learning Systems Book [MIT-HARVARD]]]> https://mklab.gr/showthread.php?tid=1063 Sun, 12 Jul 2026 01:50:36 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1063 Machine Learning Systems Book 
BY [MIT-HARVARD]

Summary

The Machine Learning Systems book (MLSysBook) is an open-source educational resource that explores the engineering principles behind building, deploying, and maintaining modern machine learning systems. Rather than focusing only on machine learning algorithms and mathematical theory, the book examines the complete lifecycle of ML applications, including data management, model development, software infrastructure, hardware considerations, deployment pipelines, monitoring, reliability, security, and ethical challenges. It emphasizes that successful machine learning systems require the integration of multiple disciplines, such as computer science, statistics, software engineering, and systems design.

Written as a practical guide for students, researchers, and engineers, the book presents machine learning as an end-to-end systems problem rather than simply a modeling task. It highlights important concepts such as scalability, efficiency, reproducibility, robustness, and the gap between experimental models and real-world production environments. Through examples and discussions of real-world applications, MLSysBook explains why building effective AI systems requires careful consideration of both technical and societal factors. 

The project aims to make machine learning systems knowledge more accessible and openly available, helping readers understand the challenges involved in creating dependable AI technologies. Its broader importance lies in preparing the next generation of practitioners to design machine learning systems that are not only accurate, but also practical, responsible, and capable of operating reliably at scale.

BOOK PAGE]]> Machine Learning Systems Book 
BY [MIT-HARVARD]

Summary

The Machine Learning Systems book (MLSysBook) is an open-source educational resource that explores the engineering principles behind building, deploying, and maintaining modern machine learning systems. Rather than focusing only on machine learning algorithms and mathematical theory, the book examines the complete lifecycle of ML applications, including data management, model development, software infrastructure, hardware considerations, deployment pipelines, monitoring, reliability, security, and ethical challenges. It emphasizes that successful machine learning systems require the integration of multiple disciplines, such as computer science, statistics, software engineering, and systems design.

Written as a practical guide for students, researchers, and engineers, the book presents machine learning as an end-to-end systems problem rather than simply a modeling task. It highlights important concepts such as scalability, efficiency, reproducibility, robustness, and the gap between experimental models and real-world production environments. Through examples and discussions of real-world applications, MLSysBook explains why building effective AI systems requires careful consideration of both technical and societal factors. 

The project aims to make machine learning systems knowledge more accessible and openly available, helping readers understand the challenges involved in creating dependable AI technologies. Its broader importance lies in preparing the next generation of practitioners to design machine learning systems that are not only accurate, but also practical, responsible, and capable of operating reliably at scale.

BOOK PAGE]]> <![CDATA[Foundations of Data Science [Blum]]]> https://mklab.gr/showthread.php?tid=1015 Fri, 10 Jul 2026 01:34:47 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1015 Foundations of Data Science
BY Avrim Blum

Summary

The landscape of modern mathematics is undergoing a profound evolution, characterized as a "technological turn" that alters both the creation of mathematical knowledge and the social structures supporting it. Driven by advancements in artificial intelligence for mathematics, this shift centers on tools like Interactive Theorem Provers (ITPs) and neural large language models (LLMs). 

Traditionally, mathematical communities rely on peer review to verify informal proofs, yet the increasing complexity of contemporary research challenges human scrutiny. ITPs mitigate human fallibility by translating informal mathematical concepts into strict formal languages for computer verification. While these software platforms drastically heighten certainty, they introduce unique vulnerabilities, such as latent translation defects between intuitive reasoning and formal execution.

Beyond individual verification, this technological integration fundamentally reshapes collaborative research dynamics, replacing traditional interpersonal trust with algorithmic verification. This shift enables large-scale, asynchronous, and modular crowdsourcing projects, effectively forming hybrid epistemic networks that combine human intelligence with specialized software tools. Concurrently, the rise of neural architectures and neurosymbolic systems pushes automated reasoning to competitive heights, as demonstrated by automated medal-level solutions to Olympiad problems and the discovery of novel mathematical conjectures.

 However, this deployment sparks serious concerns regarding systemic errors, intellectual property rights, environmental sustainability, and the potential decline of core human competencies. Ultimately, understanding these complex interactions between human minds and machine capabilities remains critical as automated theorem proving and digital collaboration platforms permanently reshape the parameters of rigorous mathematical inquiry.

BOOK [PDF]]]> Foundations of Data Science
BY Avrim Blum

Summary

The landscape of modern mathematics is undergoing a profound evolution, characterized as a "technological turn" that alters both the creation of mathematical knowledge and the social structures supporting it. Driven by advancements in artificial intelligence for mathematics, this shift centers on tools like Interactive Theorem Provers (ITPs) and neural large language models (LLMs). 

Traditionally, mathematical communities rely on peer review to verify informal proofs, yet the increasing complexity of contemporary research challenges human scrutiny. ITPs mitigate human fallibility by translating informal mathematical concepts into strict formal languages for computer verification. While these software platforms drastically heighten certainty, they introduce unique vulnerabilities, such as latent translation defects between intuitive reasoning and formal execution.

Beyond individual verification, this technological integration fundamentally reshapes collaborative research dynamics, replacing traditional interpersonal trust with algorithmic verification. This shift enables large-scale, asynchronous, and modular crowdsourcing projects, effectively forming hybrid epistemic networks that combine human intelligence with specialized software tools. Concurrently, the rise of neural architectures and neurosymbolic systems pushes automated reasoning to competitive heights, as demonstrated by automated medal-level solutions to Olympiad problems and the discovery of novel mathematical conjectures.

 However, this deployment sparks serious concerns regarding systemic errors, intellectual property rights, environmental sustainability, and the potential decline of core human competencies. Ultimately, understanding these complex interactions between human minds and machine capabilities remains critical as automated theorem proving and digital collaboration platforms permanently reshape the parameters of rigorous mathematical inquiry.

BOOK [PDF]]]> <![CDATA[Mathematical methods of reinforcement learning [Belomestny]]]> https://mklab.gr/showthread.php?tid=1011 Fri, 10 Jul 2026 00:53:49 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=1011 Mathematical methods of reinforcement learning 
by Denis Belomestny

Summary

This comprehensive literature review organizes the foundational mathematical frameworks that support modern reinforcement learning (RL) algorithms. Moving beyond simple heuristic designs, the authors trace how probability theory, optimization, and operator theory provide rigorous convergence guarantees and performance bounds across various learning paradigms. 

By grounding sequential decision-making in the foundational theory of Markov decision processes (MDPs), the survey unifies classical dynamic programming with modern stochastic approximation. It thoroughly explores diverse structural environments—ranging from tabular representations to complex function approximation using deep neural networks—and analyzes finite-horizon, discounted, and average-reward settings.

A major contribution of this work is its mathematical harmonization of the core exploration-exploitation dilemma. The review connects classical statistical estimation with online learning, highlighting how a regret-based viewpoint drives advanced exploration strategies like optimistic exploration. Furthermore, the survey evaluates both model-based approaches, which explicitly map out transition dynamics, and model-free techniques, which optimize policies directly through environment interactions. 

Ultimately, this work provides a rigorous theoretical backbone for the AI community, demonstrating how analytical tools like concentration inequalities and entropy-regularized policy gradients translate abstract mathematical concepts into stable, scalable, and highly efficient real-world artificial intelligence applications.


ARTICLE [PDF]]]> Mathematical methods of reinforcement learning 
by Denis Belomestny

Summary

This comprehensive literature review organizes the foundational mathematical frameworks that support modern reinforcement learning (RL) algorithms. Moving beyond simple heuristic designs, the authors trace how probability theory, optimization, and operator theory provide rigorous convergence guarantees and performance bounds across various learning paradigms. 

By grounding sequential decision-making in the foundational theory of Markov decision processes (MDPs), the survey unifies classical dynamic programming with modern stochastic approximation. It thoroughly explores diverse structural environments—ranging from tabular representations to complex function approximation using deep neural networks—and analyzes finite-horizon, discounted, and average-reward settings.

A major contribution of this work is its mathematical harmonization of the core exploration-exploitation dilemma. The review connects classical statistical estimation with online learning, highlighting how a regret-based viewpoint drives advanced exploration strategies like optimistic exploration. Furthermore, the survey evaluates both model-based approaches, which explicitly map out transition dynamics, and model-free techniques, which optimize policies directly through environment interactions. 

Ultimately, this work provides a rigorous theoretical backbone for the AI community, demonstrating how analytical tools like concentration inequalities and entropy-regularized policy gradients translate abstract mathematical concepts into stable, scalable, and highly efficient real-world artificial intelligence applications.


ARTICLE [PDF]]]> <![CDATA[Deep Learning for Astrophysics [NASA]]]> https://mklab.gr/showthread.php?tid=993 Thu, 09 Jul 2026 02:08:48 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=993 Deep Learning for Astrophysics
BY [NASA]

Summary

As modern astrophysics enters an era of unprecedented data collection, the bottleneck to unlocking the secrets of the cosmos is no longer the availability of advanced computational tools, but rather the community's technical literacy in utilizing them. Addressing this educational barrier, Deep Learning for Astrophysics serves as an open-access, comprehensive textbook curated by the NASA Cosmic Origins AI/ML Science and Technology Interest Group. 
The resource bridges the gap between traditional statistical methodologies and cutting-edge machine learning by providing a modular, domain-specific guide tailored for researchers and graduate students. Structured into twenty-three chapters across six core sections, the text sequentially demystifies computational foundations, advanced neural network architectures, generative modeling, and autonomous large-language-model agents. Rather than offering abstract theoretical surveys, it pairs core artificial intelligence principles with executable coding notebooks that utilize authentic astronomical data—such as galaxy images, stellar streams, and transient light curves—ensuring that practitioners grasp both the capabilities and the nuanced failure modes of these models.

Ultimately, the text argues that the long-term success of artificial intelligence in astronomy depends on transparency, physical testability, and architectural choices that inherently respect physical laws, rather than a superficial reliance on raw predictive accuracy. By shifting the paradigm to treat artificial intelligence as a natural extension of established statistical practices, the book works to dismantle community skepticism rooted in the opaque "black box" nature of complex models.

 Beyond simply training researchers to build better networks, it fosters widespread literacy that is equally essential for peers tasked with reviewing and interpreting AI-laden science. As upcoming global observatories prepare to harvest vast pipelines of cosmic data, this open-source educational framework provides the foundational literacy required to maximize the scientific return of next-generation space missions and deepen our collective understanding of the universe.


BOOK]]> Deep Learning for Astrophysics
BY [NASA]

Summary

As modern astrophysics enters an era of unprecedented data collection, the bottleneck to unlocking the secrets of the cosmos is no longer the availability of advanced computational tools, but rather the community's technical literacy in utilizing them. Addressing this educational barrier, Deep Learning for Astrophysics serves as an open-access, comprehensive textbook curated by the NASA Cosmic Origins AI/ML Science and Technology Interest Group. 
The resource bridges the gap between traditional statistical methodologies and cutting-edge machine learning by providing a modular, domain-specific guide tailored for researchers and graduate students. Structured into twenty-three chapters across six core sections, the text sequentially demystifies computational foundations, advanced neural network architectures, generative modeling, and autonomous large-language-model agents. Rather than offering abstract theoretical surveys, it pairs core artificial intelligence principles with executable coding notebooks that utilize authentic astronomical data—such as galaxy images, stellar streams, and transient light curves—ensuring that practitioners grasp both the capabilities and the nuanced failure modes of these models.

Ultimately, the text argues that the long-term success of artificial intelligence in astronomy depends on transparency, physical testability, and architectural choices that inherently respect physical laws, rather than a superficial reliance on raw predictive accuracy. By shifting the paradigm to treat artificial intelligence as a natural extension of established statistical practices, the book works to dismantle community skepticism rooted in the opaque "black box" nature of complex models.

 Beyond simply training researchers to build better networks, it fosters widespread literacy that is equally essential for peers tasked with reviewing and interpreting AI-laden science. As upcoming global observatories prepare to harvest vast pipelines of cosmic data, this open-source educational framework provides the foundational literacy required to maximize the scientific return of next-generation space missions and deepen our collective understanding of the universe.


BOOK]]> <![CDATA[Feature Engineering and Selection]]> https://mklab.gr/showthread.php?tid=694 Tue, 23 Jun 2026 18:57:22 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=694 BY  Max Kuhn and Kjell Johnson

Summary

Feature Engineering and Selection: A Practical Approach for Predictive Models explores one of the most important steps in modern machine learning: transforming raw data into meaningful information that allows predictive models to perform better. The book explains how feature engineering and feature selection help data scientists improve model accuracy by creating better representations of variables, identifying useful patterns, and avoiding unnecessary complexity. 
In today’s world of artificial intelligence and data-driven decision making, having large amounts of data is not enough — the quality and structure of that data determine the success of a model. This book provides practical insights into predictive modeling, data preprocessing, visualization, handling missing information, and selecting the most valuable features. 
A valuable resource for machine learning enthusiasts, data analysts, and AI professionals, it highlights how thoughtful data preparation can turn ordinary datasets into powerful tools for prediction and discovery. By combining theory with practical techniques, the book shows why feature engineering remains a key skill in building reliable and effective artificial intelligence systems. 

BOOK]]> BY  Max Kuhn and Kjell Johnson

Summary

Feature Engineering and Selection: A Practical Approach for Predictive Models explores one of the most important steps in modern machine learning: transforming raw data into meaningful information that allows predictive models to perform better. The book explains how feature engineering and feature selection help data scientists improve model accuracy by creating better representations of variables, identifying useful patterns, and avoiding unnecessary complexity. 
In today’s world of artificial intelligence and data-driven decision making, having large amounts of data is not enough — the quality and structure of that data determine the success of a model. This book provides practical insights into predictive modeling, data preprocessing, visualization, handling missing information, and selecting the most valuable features. 
A valuable resource for machine learning enthusiasts, data analysts, and AI professionals, it highlights how thoughtful data preparation can turn ordinary datasets into powerful tools for prediction and discovery. By combining theory with practical techniques, the book shows why feature engineering remains a key skill in building reliable and effective artificial intelligence systems. 

BOOK]]> <![CDATA[Introduction to Machine Learning Interviews]]> https://mklab.gr/showthread.php?tid=693 Tue, 23 Jun 2026 18:54:21 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=693 Introduction to Machine Learning Interviews
by   Chip Huyen


Summary

Machine Learning Interviews Book is a practical guide designed to help students, engineers, and aspiring AI professionals better understand the world of machine learning interviews and the skills companies look for when hiring. Based on real experiences from both candidates and interviewers, the book explores the hiring process, different ML roles, technical expectations, and the mindset needed to succeed in competitive technology environments. 
More than just an interview preparation resource, this book highlights the importance of building strong foundations in machine learning concepts, problem-solving abilities, and practical engineering knowledge. With hundreds of questions covering key ML topics, it helps readers identify knowledge gaps and improve their understanding of artificial intelligence in a structured way. 
For anyone interested in a career in AI, data science, or machine learning engineering, this resource offers valuable insights into how the industry evaluates talent and what skills matter most. It connects theory with real-world expectations, making it a useful roadmap for navigating the growing field of machine learning and preparing for future opportunities in technology. 

BOOK]]> Introduction to Machine Learning Interviews
by   Chip Huyen


Summary

Machine Learning Interviews Book is a practical guide designed to help students, engineers, and aspiring AI professionals better understand the world of machine learning interviews and the skills companies look for when hiring. Based on real experiences from both candidates and interviewers, the book explores the hiring process, different ML roles, technical expectations, and the mindset needed to succeed in competitive technology environments. 
More than just an interview preparation resource, this book highlights the importance of building strong foundations in machine learning concepts, problem-solving abilities, and practical engineering knowledge. With hundreds of questions covering key ML topics, it helps readers identify knowledge gaps and improve their understanding of artificial intelligence in a structured way. 
For anyone interested in a career in AI, data science, or machine learning engineering, this resource offers valuable insights into how the industry evaluates talent and what skills matter most. It connects theory with real-world expectations, making it a useful roadmap for navigating the growing field of machine learning and preparing for future opportunities in technology. 

BOOK]]> <![CDATA[Elements of Statistical Learning]]> https://mklab.gr/showthread.php?tid=692 Tue, 23 Jun 2026 18:51:10 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=692 Elements of Statistical Learning
BY  Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Summary

In the world of machine learning, data science, and artificial intelligence, few books have had as much influence as The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. This landmark work explores how computers can learn from data, make predictions, and uncover hidden patterns using statistical methods. Rather than focusing solely on formulas, the book builds a deep understanding of the concepts behind modern predictive modeling, helping readers see how data-driven decisions are made across fields such as healthcare, finance, marketing, and scientific research.
What makes this book especially valuable is its broad coverage of key topics that power today’s AI revolution, including neural networks, support vector machines, decision trees, random forests, clustering, and ensemble learning. Readers gain insight into both supervised and unsupervised learning techniques, learning when and why different algorithms succeed. The authors connect these methods through a common statistical framework, making complex ideas easier to understand and compare.
More than two decades after its first publication, the book remains a foundational resource for students, researchers, and professionals seeking a deeper understanding of machine learning. Its lasting impact can be seen in the way it bridges traditional statistics with modern AI, providing the theoretical foundations behind many technologies that shape our daily lives. For anyone interested in predictive analytics, data mining, or the future of intelligent systems, this book offers a roadmap to understanding one of the most important fields of the digital age.

BOOK]]> Elements of Statistical Learning
BY  Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Summary

In the world of machine learning, data science, and artificial intelligence, few books have had as much influence as The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. This landmark work explores how computers can learn from data, make predictions, and uncover hidden patterns using statistical methods. Rather than focusing solely on formulas, the book builds a deep understanding of the concepts behind modern predictive modeling, helping readers see how data-driven decisions are made across fields such as healthcare, finance, marketing, and scientific research.
What makes this book especially valuable is its broad coverage of key topics that power today’s AI revolution, including neural networks, support vector machines, decision trees, random forests, clustering, and ensemble learning. Readers gain insight into both supervised and unsupervised learning techniques, learning when and why different algorithms succeed. The authors connect these methods through a common statistical framework, making complex ideas easier to understand and compare.
More than two decades after its first publication, the book remains a foundational resource for students, researchers, and professionals seeking a deeper understanding of machine learning. Its lasting impact can be seen in the way it bridges traditional statistics with modern AI, providing the theoretical foundations behind many technologies that shape our daily lives. For anyone interested in predictive analytics, data mining, or the future of intelligent systems, this book offers a roadmap to understanding one of the most important fields of the digital age.

BOOK]]> <![CDATA[Patterns, Predictions and Actions]]> https://mklab.gr/showthread.php?tid=691 Tue, 23 Jun 2026 18:48:17 +0300 mklabgr]]> https://mklab.gr/showthread.php?tid=691 Patterns, Predictions and Actions
BY  Moritz Hardt and Benjamin Recht

Summary 

Machine learning has transformed the modern world, but understanding how it actually works can still feel overwhelming. Patterns, Predictions, and Actions takes a refreshing approach by explaining machine learning through the simple idea that data contains patterns, patterns enable predictions, and predictions guide decisions. Rather than focusing only on algorithms and equations, the book explores how machine learning evolved, why it matters, and how it influences everyday life. It highlights the foundations of supervised learning, optimization, data representation, and decision-making in a way that connects theory with real-world applications. 
What makes this machine learning book stand out is its broader perspective. Beyond technical concepts, it examines the importance of datasets, causal reasoning, reinforcement learning, and the consequences of using AI-driven systems in society. Readers are encouraged to think critically about fairness, reliability, and the impact of automated decisions on fields such as healthcare, education, and public policy. By combining mathematical foundations with practical insights, the book offers a balanced introduction for students, researchers, and technology enthusiasts alike. 
For anyone interested in artificial intelligence, data science, or the future of machine learning, this book provides more than just technical knowledge—it offers a framework for understanding how predictive models shape the world around us. Its emphasis on responsible AI, informed decision-making, and the relationship between data and action makes it a valuable resource in an era where machine learning continues to influence business, science, and society at an unprecedented scale. 

BOOK]]> Patterns, Predictions and Actions
BY  Moritz Hardt and Benjamin Recht

Summary 

Machine learning has transformed the modern world, but understanding how it actually works can still feel overwhelming. Patterns, Predictions, and Actions takes a refreshing approach by explaining machine learning through the simple idea that data contains patterns, patterns enable predictions, and predictions guide decisions. Rather than focusing only on algorithms and equations, the book explores how machine learning evolved, why it matters, and how it influences everyday life. It highlights the foundations of supervised learning, optimization, data representation, and decision-making in a way that connects theory with real-world applications. 
What makes this machine learning book stand out is its broader perspective. Beyond technical concepts, it examines the importance of datasets, causal reasoning, reinforcement learning, and the consequences of using AI-driven systems in society. Readers are encouraged to think critically about fairness, reliability, and the impact of automated decisions on fields such as healthcare, education, and public policy. By combining mathematical foundations with practical insights, the book offers a balanced introduction for students, researchers, and technology enthusiasts alike. 
For anyone interested in artificial intelligence, data science, or the future of machine learning, this book provides more than just technical knowledge—it offers a framework for understanding how predictive models shape the world around us. Its emphasis on responsible AI, informed decision-making, and the relationship between data and action makes it a valuable resource in an era where machine learning continues to influence business, science, and society at an unprecedented scale. 

BOOK]]>