“Generative AI with Python: The Developer’s Guide to Pretrained LLMs, Vector Databases, Retrieval Augmented Generation, and Agentic Systems” Available at http://
amzn.to/4sa1MiY
@kirkdborne
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Python Generative AI Guide Covers LLMs, Vector Databases, and Agents
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Scikit-Learn Cookbook Covers Preprocessing and Machine Learning Workflows
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Scikit-learn Cookbook — 80+ recipes for Machine Learning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML 𝓒𝓸𝓷𝓽𝓮𝓷𝓽𝓼:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality -
Python Exercises Teach Mathematical Foundations of AI
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Basic Mathematical Foundations of AI — Hands on with Python: http://
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Agentic AI Engineering Guide Covers Reasoning Loops and Real-World Deployment
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Agentic AI Engineering for Systems that Reason and Act Autonomously – Design, Build, and Prompt LLM-Based Agents for Real-World Deployment: http://
amzn.to/4eXSPnC Main Topics:
• How autonomous systems differ from automation
• Reasoning loops and core components
• Designing -
Beginner Guide Explains Building and Fine-Tuning Small Language Models
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"How to Build and Fine‐Tune a Small Language Model: A Step-by-Step Guide for Beginners, Researchers, and Non-Programmers" Available at http://
amzn.to/4aV0amA Build your own AI—without a PhD, expensive hardware, or industry-level resources. Whether you’re a beginner, a student, -
Supercomputing Architectures for Scaling Deep Learning
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Supercomputing for AI — Foundations, Architectures, and Scaling Deep Learning. [804-page masterpiece] Get it: http://
amzn.to/4qS4pFz Git it: https://
github.com/jorditorresBCN
/supercomputing-for-ai
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Graph Machine Learning Book Uses PyTorch Geometric and DGL
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2nd Edition at http://
amzn.to/45Y3LyI Graph Machine Learning — the latest advancements in graph data to build robust machine learning algorithms. 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library -
Preparing Messy Data for Machine Learning Requires Profiling and Normalization
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If your data is a mess, then (to prepare for machine learning and other analysis & inference methods) you need to spend a lot of time doing a lot of data profiling, data typing, data transformations (e.g., scaling, zero-centering, normalizations, adjusting for different units,
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Nine Distance Metrics for Machine Learning and Their Trade-Offs
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9 Distance Metrics used in Data Science and Machine Learning (advantages & pitfalls): https://
maartengrootendorst.com/blog/distances/
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#ML #Statistics #Mathematics #DataScientist ——
See 730+ page book "Encyclopedia of Distances" (3rd edition): https://
amzn.to/3skDOWm -
Architectural Patterns for Enterprise Multi-Agent AI and RAG Systems
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"Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" See it at http://
amzn.to/3MaHy8T 𝕋𝕒𝕓𝕝𝕖 𝕠𝕗 ℂ𝕠𝕟𝕥𝕖𝕟𝕥𝕤:
GenAI in the Enterprise: Landscape,