New Release! "Machine Learning Foundations, Volume 1: Supervised Learning" – available at http://
amzn.to/4syhPal Benefits: Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques
@kirkdborne
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New Machine Learning Foundations Book: Supervised Learning Concepts
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Mastering PyTorch: Create and Deploy Deep Learning Models
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"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" – http://amzn.to/40IFEQR via @PacktDataML —————
#AI #ML #MachineLearning #DataScience #DataScientist #GenAI -
Essential Resources for Learning Causal Inference in Data Science
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A First Course in Causal Inference: http://
arxiv.org/abs/2305.18793 [490-page PDF download] + Also see the book "Causal Inference in Statistics: A Primer" at http://
amzn.to/3Mrm2wO by @yudapearl #Probability #Mathematics #DataScience #ML #MachineLearning #DataScientist #DataAnalysis -
Complete Machine Learning Engineer Cookbook: Python AI Developer Learning Path
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AI Mastery >> The Complete Machine Learning Engineer Cookbook for Everyone — Become an AI Developer with Python: http://
amzn.to/3XZTzQS Complete 5-part Learning Path:
1 – Build Your Foundation
2 – Assemble Your ML Toolkit
3 – Discover Deep Learning & Generative AI
4 – Master -
Hands-On Mathematical Optimization with Python: Key Ingredients and Modeling Choices
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Hands-On Mathematical Optimization with Python: http://
amzn.to/4b3VADe “…presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to -
Tensor Decompositions for Data Science and Computational Science
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Tensor Decompositions for Data Science [and Computational Science]: http://
amzn.to/4s2B5g1
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#ML #MachineLearning #DataScientist #DataScience #Mathematics
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Note: Extensive background materials in linear algebra, optimization, probability, and statistics are included as -
New Book on Agentic Architectural Patterns for Multi-Agent Systems
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5- release from @PacktDataML at http://
amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
GenAI in the Enterprise: Landscape, -
New Multi-Agent AI Systems Design Book Release from Packt
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New release from @PacktDataML available at: http://
amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents:
Introduction to Generative AI and AI -
New Machine Learning Systems Guide: Building, Deploying, Scaling Production
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New release! >> Shipping Machine Learning Systems — Practical Guide to Building, Deploying, and Scaling in Production: http://
amzn.to/4snEtlS Bridges the gap from Theoretical to Practical Machine Learning, while avoiding the Theatrical. -
Basic Mathematical Foundations of AI with Python Hands-On Guide
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Basic Mathematical Foundations of AI — Hands on with Python: http://
amzn.to/4c0w6pz