Fourth Industrial Revolution

In this post, I explain Confusion Matrix in detail. Learn Confusion Matrix Definition and Intuition, Claim Approval Example, Confusion Matrix Table Layout, Core Concepts Explained (TP, TN, FP, FN), Confusion Matrix Formulae, Derived Metrics from the Confusion Matrix (Precision, Recall, F1, Specificity), and Visualization and Code. If you want to additionally learn about the following confusion matrix topics or comment, you can do so on my original Confusion Matrix article on LinkedIn here . Thresholding, ROC and PR Curves, Imbalanced Data and the Accuracy Paradox, Multiclass and Multi-Label Confusion Matrices (Visualization and Interpretation), Cost-Sensitive Decisions: Cost Matrix, Business Tradeoffs, and Setting Operational Thresholds, Calibration, Confidence, and When to Trust Model Probabilities, Practical Tips and Troubleshooting (Data leakage, label noise, sampling effects) — confusion matrix tutorial, debugging checklist for AI Developers and AI QA Testers, Ethics, Fairness an...

In this post, I explain LLMs in Python, Python Setup & Installation, Inference with Transformers, Calling ChatGPT API in Python, Python Local Deployment with Hugging Face Models, Prompt Engineering in Python and FineTuning & Custom Training (including LoRA). You can test your knowledge of LLMs in Python by attempting the Quiz after every set of Questions and Answers. If you want my complete Introduction to LLMs in Python document that additionally includes the following important topics, you can message me on LinkedIn : Python Advanced Techniques (Streaming, Batching & Callbacks), Python Efficiency & #Optimization (quantization, distillation, and parameter‑efficient tuning), Integration & Deployment Workflows, LLMs in Python Best Practices & Troubleshooting, and consolidated Introduction to LLMs in Python Quiz (with answer explanations to reinforce learning). Question : What do I mean by "Introduction to LLMs in Python"? Answer : Introduction to LL...