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“The Effect: An Introduction to Research Design and Causality” [2nd edition] is an excellent teaching text about research design, specifically concerning research that uses observational data to make a causal inference: link.amazon/B09IzNjKf

"Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond" — link.amazon/B0hQdgN3x ————— #AI #ML #MachineLearning #DataScience #DataScientist

Yes, graphs are everywhere! Link up with this reality! Graph Machine Learning — the latest advancements in graph data to build robust machine learning algorithms. 2nd Edition at link.amazon/B0iMMOu7x 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: 🟠Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) 🔵Explore GML frameworks and their main characteristics 🟠Leverage LLMs for machine learning on graphs and learn about temporal learning

Graph Machine Learning Essentials — Foundations, Hands-On Implementation, Graph Neural Networks, PyTorch Geometric, and Applied Use Cases: link.amazon/B06U0qXFq

🌟🌟🌟🌟🌟 "Time Series with PyTorch: Modern Deep Learning Toolkit for Real-World Forecasting Challenges", available at link.amazon/B06JjA1BG

Siri AI Visual Bible — A step-by-step, screenshot-by-screenshot guide to getting things done across your Apple devices: link.amazon/B091XSqf5

The Claude Certified Architect Study Guide — Prepare for CCAR-F and CCAR-P with production scenarios and architectural judgement: link.amazon/B0iuEiUwz

Building the Software Factory — Scaling Reliable AI Coding Practices from Individual developers to Teams: link.amazon/B0fWy3VXB

Deep Learning from Curiosity to Mastery — Volume 1, Foundations of Neural Networks: link.amazon/B0953tV89

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP) Get it here: link.amazon/B07HTcXgJ

Artificial Intelligence for Science — A Deep Learning Revolution: link.amazon/B0bAjzUqx —————— #AI #ML #DataScience #MachineLearning

Most enterprise strategy requires Design Thinking and an Agile approach to UX: “Think Big, Start Small”, “Start with the End in Mind”, and “Fail Fast to Learn Fast.” 📈🌟🏆🥇 Start with this beautiful "Design Thinking Playbook" at link.amazon/B0fTCLiCA ✨✨✨✨✨

Mathematics humor… ⤵️ 📕 Get Now: link.amazon/B00WUNWuF

Power Law Strategy — Why Few Businesses Scale and Millions Don't: link.amazon/B0hl0a1bo

Model to Meaning — How to interpret Statistical Models with R and Python: link.amazon/B00BkmSwW — Concrete workflows, task-specific software, and detailed case studies, presented using real-world data and code examples.

Enter “Statland” — and into a world of curiosity where logic becomes landscape, shadows hold patterns, and every valley hums with hidden meaning: link.amazon/B05CDgr1j #Statistics #DataLiteracy #DataScience

Ontology Pipeline — Framework for Knowledge Engineering and Building Knowledge Infrastructures: link.amazon/B01Mm88qZ Amazon description: “One of the biggest shifts happening in our industry is that enterprise data management is expanding into enterprise knowledge management. AI is accelerating that transition because it depends on more than well-managed data. It depends upon context, shared meaning, trusted relationships, and the ability to organize the vast stores of unstructured knowledge that exist across every large enterprise. As AI becomes the primary interface to enterprise information, knowledge management will become the foundation that unlocks trustworthy AI at scale.”

The Game Theory Method — Concepts, Worked Examples, and Exercises for Strategic Thinkers | Nash Equilibrium, Mixed Strategies, Sequential and Bayesian Games: link.amazon/B0aOBGyzf

Benford’s Law: link.amazon/B09mqT6VE Fraud detection is just one of its many practical powerful applications! Amazon Summary: “This book provides the first comprehensive treatment of Benford's law, the surprising logarithmic distribution of significant digits discovered in the late nineteenth century. Establishing the mathematical and statistical principles that underpin this intriguing phenomenon, the text combines up-to-date theoretical results with overviews of the law’s colorful history, rapidly growing body of empirical evidence, and wide range of applications. An Introduction to Benford’s Law begins with basic facts about significant digits, Benford functions, sequences, and random variables, including tools from the theory of uniform distribution. After introducing the scale-, base-, and sum-invariance characterizations of the law, the book develops the significant-digit properties of both deterministic and stochastic processes, such as iterations of functions, powers of matrices, differential equations, and products, powers, and mixtures of random variables. Two concluding chapters survey the finitely additive theory and the flourishing applications of Benford’s law. Carefully selected diagrams, tables, and close to 150 examples illuminate the main concepts throughout. The text includes many open problems, in addition to dozens of new basic theorems and all the main references. A distinguishing feature is the emphasis on the surprising ubiquity and robustness of the significant-digit law. This text can serve as both a primary reference and a basis for seminars and courses.”