Fatemeh Jahangard is a technology and AI writer at Pinerook, covering artificial intelligence, emerging technologies, and the companies turning new ideas into practical products. Her work focuses on how technological shifts influence startups, industries, and the way modern businesses are built.
Expertise
- Artificial Intelligence
- Emerging Technology
- Digital Innovation
- AI Startups
- Enterprise Technology
Stories by Fatemeh Jahangard (77)
- AI
Text to Video AI: How to Generate Videos From Text
Learn how to turn text prompts into AI videos, compare leading video generators, and improve results with better prompts, camera controls, and editing techniques.
Fatemeh Jahangard · Oct 29, 2026 · 12 min read - AI
AI Video Generation: How It Works, Methods, and Tools
AI video generation is reshaping digital content creation. Explore how it works, the methods and tools behind it, and how businesses can produce videos with AI.
Fatemeh Jahangard · Oct 29, 2026 · 14 min read - AI
Permutation Feature Importance: Examples and Python Guide
Learn how Permutation Feature Importance measures model reliance on input features, with practical examples, Python guidance, score interpretation, and evaluation techniques.
Fatemeh Jahangard · Oct 28, 2026 · 14 min read - AI
Partial Dependence Plots: Examples and Python Guide
Learn how Partial Dependence Plots reveal feature relationships in machine learning models, with practical examples, Python guidance, and techniques for interpreting model predictions.
Fatemeh Jahangard · Oct 28, 2026 · 12 min read - AI
Counterfactual Explanations: How They Work and Examples
Explore how counterfactual explanations reveal changes that alter AI predictions, with practical examples, generation methods, DiCE, and actionable insights.
Fatemeh Jahangard · Oct 27, 2026 · 13 min read - AI
LIME Explainable AI: How It Works, Examples, and Python
Discover how LIME explains individual machine learning predictions, with practical examples, Python implementation guidance, feature importance analysis, and explanation reliability.
Fatemeh Jahangard · Oct 27, 2026 · 12 min read - AI
SHAP Values Explained: How to Interpret Results and Plots
SHAP values reveal how individual features influence machine learning predictions. Learn to interpret positive and negative contributions, read SHAP plots, and understand model decisions.
Fatemeh Jahangard · Oct 26, 2026 · 12 min read - AI
Explainable AI (XAI): How It Works, Methods, and Real World Examples
Explainable AI reveals how machine learning models make decisions. Explore its methods, real world applications, and the techniques used to interpret AI predictions.
Fatemeh Jahangard · Oct 26, 2026 · 11 min read - AI
Synthetic Test Data: Examples, Generation, and Uses
Learn how to generate synthetic test data for software testing, with practical examples, generation methods, and techniques for reliable automated testing.
Fatemeh Jahangard · Oct 25, 2026 · 11 min read - AI
Synthetic Data for Computer Vision: Generation, Training, and Applications
Explore how synthetic data is generated for computer vision, from 3D simulation to AI-generated images, and how it improves model training, annotation, and real-world performance.
Fatemeh Jahangard · Oct 25, 2026 · 14 min read - AI
Synthetic Data Generation Tools: Best Options Compared
A practical comparison of the best synthetic data generation tools, covering features, pricing, open source options, limitations, and use cases for AI training and testing.
Fatemeh Jahangard · Oct 24, 2026 · 13 min read - AI
Synthetic Data Quality: How to Evaluate and Improve It
A practical guide to evaluating synthetic data quality through statistical fidelity, machine learning utility, privacy risk, and data validation methods.
Fatemeh Jahangard · Oct 24, 2026 · 12 min read - AI
Synthetic Data vs Real Data: Which Is Better for AI Training?
A practical comparison of synthetic and real data in AI training, covering data quality, accuracy, cost, privacy, and hybrid datasets for LLM fine-tuning.
Fatemeh Jahangard · Oct 23, 2026 · 10 min read - AI
Synthetic Tabular Data: How to Generate and Evaluate It
A practical guide to synthetic tabular data generation, covering statistical models, CTGAN, TVAE, data relationships, quality evaluation, and privacy.
Fatemeh Jahangard · Oct 22, 2026 · 12 min read - AI
Synthetic Data: How It Works, Types, Methods, and Real-World Applications
Synthetic data is changing how AI systems are trained and tested. Explore how it is generated, where it delivers value, and what makes it reliable.
Fatemeh Jahangard · Oct 22, 2026 · 13 min read - AI
Instruction Tuning: How LLMs Learn to Follow Instructions
A practical guide to instruction tuning in LLMs, covering how models learn to follow human instructions, training examples, zero-shot generalization, and evaluation.
Fatemeh Jahangard · Oct 21, 2026 · 12 min read - AI
Fine-Tuning Hyperparameters: Settings and Optimization Guide for LLMs
A practical guide to fine-tuning hyperparameters for LLMs, covering learning rate, batch size, epochs, LoRA, QLoRA, and strategies for optimizing training performance.
Fatemeh Jahangard · Oct 21, 2026 · 12 min read - AI
Supervised Fine-Tuning (SFT): How It Works for LLMs
A practical guide to Supervised Fine-Tuning (SFT), covering how LLMs learn from labeled examples, training workflows, LoRA, QLoRA, and model evaluation.
Fatemeh Jahangard · Oct 20, 2026 · 11 min read - AI
Catastrophic Forgetting in AI: Causes, Examples, and Prevention
Understand catastrophic forgetting in AI, why neural networks lose previously learned knowledge, and how to detect, measure, and prevent it during LLM fine-tuning.
Fatemeh Jahangard · Oct 20, 2026 · 11 min read - AI
Fine-Tuning Dataset: How to Prepare High-Quality Training Data for LLMs
A practical guide to preparing fine-tuning datasets, covering training examples, data formats, cleaning, validation, and dataset evaluation for LLMs.
Fatemeh Jahangard · Oct 19, 2026 · 9 min read
