Dharmalingam krishna (19 résultats)

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Taschenbuch. Etat : Neu. Neuware - The first volume of A Practitioner's Journey. Eighteen chapters take you from linear algebra and probability through every classical machine-learning algorithm worth knowing - regression, trees, ensembles, SVMs, KNN, time series, and recommendation systems - with the math, the intuition, and runnable code, all in one place.…

Taschenbuch. Etat : Neu. Neuware - The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them.

Etat : New. Print on Demand.

Paperback. Etat : new. Paperback. The first volume of A Practitioner's Journey. Eighteen chapters take you from linear algebra and probability through every classical machine-learning algorithm worth knowing - regression, trees, ensembles, SVMs, KNN, time series, and recommendation systems - with the math, the intuition, and runnable code, all in one place.This is the curriculum a working ML practitioner actually needs. Most "intro to ML" books pick a side: pure math with no code, or library-call tutorials that fall apart the moment you try to apply them. Foundations of Machine Learning refuses both. Every chapter is built around a working scenario. Every code example runs. Every concept comes with both the math and the intuition.You will learn to: Reason about linear algebra, calculus, probability, and optimization the way ML uses themDerive and implement classical algorithms from first principles, not as library callsChoose the right algorithm for the right problem and explain whyEvaluate models honestly, avoid overfitting, and know when "good enough" is good enoughApply the CRISP-DM framework to a real end-to-end case studyCompanion volumes: Book 2 Machine Learning in Production covers deep learning, computer vision, and the production engineering stack. Book 3 Artificial Intelligence in Production covers LLMs, RAG, agents, and modern AI infrastructure. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them.This is not another "build a ChatGPT clone" book. It is a working engineer's guide to the techniques and engineering decisions behind production AI systems. You will start with NLP foundations and tokenization, move through transformer architectures and LLMs, build production-grade RAG pipelines with vector stores and reranking, train reward models and align LLMs with RLHF and DPO, and finish with autonomous agents, the Model Context Protocol (MCP), multi-agent coordination, and multi-modal models.You will learn to: Build and deploy production RAG with vector stores, reranking, and citation groundingFine-tune and align LLMs with RLHF, DPO, and Constitutional AIDesign autonomous agent loops with safe tool use and approval gatesCoordinate multiple agents through the Model Context Protocol (MCP)Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-premDistill large models into deployable, edge-ready footprintsCompanion volumes: Books 1 and 2 of A Practitioner's Journey cover classical ML, deep learning, and the production engineering stack - recommended as prerequisites if you are new to the field, optional if you already work in ML/AI. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Etat : new. Paperback. The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them.This is not another "build a ChatGPT clone" book. It is a working engineer's guide to the techniques and engineering decisions behind production AI systems. You will start with NLP foundations and tokenization, move through transformer architectures and LLMs, build production-grade RAG pipelines with vector stores and reranking, train reward models and align LLMs with RLHF and DPO, and finish with autonomous agents, the Model Context Protocol (MCP), multi-agent coordination, and multi-modal models.You will learn to: Build and deploy production RAG with vector stores, reranking, and citation groundingFine-tune and align LLMs with RLHF, DPO, and Constitutional AIDesign autonomous agent loops with safe tool use and approval gatesCoordinate multiple agents through the Model Context Protocol (MCP)Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-premDistill large models into deployable, edge-ready footprintsCompanion volumes: Books 1 and 2 of A Practitioner's Journey cover classical ML, deep learning, and the production engineering stack - recommended as prerequisites if you are new to the field, optional if you already work in ML/AI. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. The first volume of A Practitioner's Journey. Eighteen chapters take you from linear algebra and probability through every classical machine-learning algorithm worth knowing - regression, trees, ensembles, SVMs, KNN, time series, and recommendation systems - with the math, the intuition, and runnable code, all in one place.This is the curriculum a working ML practitioner actually needs. Most "intro to ML" books pick a side: pure math with no code, or library-call tutorials that fall apart the moment you try to apply them. Foundations of Machine Learning refuses both. Every chapter is built around a working scenario. Every code example runs. Every concept comes with both the math and the intuition.You will learn to: Reason about linear algebra, calculus, probability, and optimization the way ML uses themDerive and implement classical algorithms from first principles, not as library callsChoose the right algorithm for the right problem and explain whyEvaluate models honestly, avoid overfitting, and know when "good enough" is good enoughApply the CRISP-DM framework to a real end-to-end case studyCompanion volumes: Book 2 Machine Learning in Production covers deep learning, computer vision, and the production engineering stack. Book 3 Artificial Intelligence in Production covers LLMs, RAG, agents, and modern AI infrastructure. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…