Claims Browser

Complex AI concepts like softmax, temperature, and top-p sampling are clarified with code-linked explanations and visual workflows.

"Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, while visual workflows make pipelines and scoring methods easier to follow."

The book offers a guided, project-driven learning experience rather than a broad survey.

"Reading the book feels like following a guided technical build rather than a loose survey of AI topics."

Refinement methods can sometimes degrade answers, a common failure mode in reasoning models.

"The book also discusses common failure modes, including cases where refinement can make answers worse."

Self-consistency, self-refinement, Best-of-N, and training-based methods have cost and latency trade-offs that are important to understand.

"Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs."

The book implements core reasoning methods from scratch rather than using black-box library calls.

"The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls."

Readers will learn how to use machine learning and agentic AI for analysis and decision-making.

"• Machine learning and agentic AI for analysis and decision-making"

Readers will learn how to represent real-world systems as knowledge graphs.

"• Represent real-world systems as knowledge graphs"

Readers will learn how to stream IoT sensor data into a digital twin.

"• Stream IoT sensor data into a twin"

Readers will learn how to blend computer vision, OCR, and generative AI with 3D geometric models.

"• Blend computer vision, OCR, and generative AI with 3D geometric models"

Readers will learn how to create digital representations of physical systems.

"• Create digital representations of physical systems"

Readers will learn how to define clear business objectives for digital twins.

"• Define clear business objectives for digital twins"

The book covers mastering XGBoost, applying deep learning to tabular data, deploying models locally and in the cloud, and building pipelines to train and maintain models.

"• Master XGBoost • Apply deep learning to tabular data • Deploy models locally and in the cloud • Build pipelines to train and maintain models"

The book 'Machine Learning for Tabular Data' is excellent.

"Excellent book from @ManningBooks >> "Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI," by @MarkRyanMkm & @lucamassaron"

This resource provides a useful introduction to outlier detection in Python.

"Outlier Detection in Python — https://t.co/vWtHhru2DW from @ManningBooks"

Insights can be discovered from data without labels using practical unsupervised machine learning.

"Insights Discovery from Data Without Labels — Practical Unsupervised Machine Learning"

The book 'Build a Large Language Model (From Scratch)' is a best seller and receives a five-star rating.

"💡Best Seller🚀"

Domain-specific small language models are efficient AI for local deployment.

""Domain-Specific Small Language Models: Efficient AI for local deployment""

Knowledge graphs and LLMs can be used together to build AI systems using connected data.

""Knowledge Graphs and LLMs in Action: Build AI systems using connected data""