October 18, 2025

Optimize NLP Models with Backtracking for Text Summarization and More

By Alireza Pourmahdavi October 18, 2025

Introduction Optimizing NLP models with backtracking can dramatically enhance the efficiency of tasks like text summarization, named entity recognition, and spell-checking. Backtracking algorithms explore different solution paths incrementally, discarding non-viable options and refining the model’s performance. However, while the approach offers powerful optimization benefits, its high computational cost and time complexity can make it less […]

Master Multiple Linear Regression in Python with Scikit-learn and Statsmodels

By Alireza Pourmahdavi October 18, 2025

Introduction Mastering multiple linear regression in Python is essential for anyone looking to build powerful predictive models. In this tutorial, we’ll dive into how to implement multiple linear regression (MLR) using Python’s popular libraries, scikit-learn and statsmodels. We’ll walk through key concepts like data preprocessing, handling multicollinearity, and performing cross-validation, all using the California Housing […]

Optimize GPU Memory in PyTorch: Boost Performance with Multi-GPU Techniques

By Alireza Pourmahdavi October 18, 2025

Introduction Efficiently managing GPU memory is crucial for optimizing performance in PyTorch, especially when working with large models and datasets. By leveraging techniques like data parallelism and model parallelism, you can distribute workloads across multiple GPUs, speeding up training and inference times. Additionally, practices such as using torch.no_grad(), emptying the CUDA cache, and utilizing 16-bit […]

Master Ridge Regression in Machine Learning: Combat Overfitting with Regularization

By Alireza Pourmahdavi October 18, 2025

Introduction Ridge regression is a powerful tool in machine learning, designed to combat overfitting by introducing a regularization penalty to the model’s coefficients. By shrinking large coefficients, it helps improve the model’s generalization ability, especially when working with datasets that have multicollinearity. This method maintains a balance between bias and variance, ultimately enhancing model stability. […]

Alireza Pourmahdavi

I’m Alireza Pourmahdavi, a founder, CEO, and builder with a background that combines deep technical expertise with practical business leadership. I’ve launched and scaled companies like Caasify and AutoVM, focusing on cloud services, automation, and hosting infrastructure. I hold VMware certifications, including VCAP-DCV and VMware NSX. My work involves constructing multi-tenant cloud platforms on VMware, optimizing network virtualization through NSX, and integrating these systems into platforms using custom APIs and automation tools. I’m also skilled in Linux system administration, infrastructure security, and performance tuning. On the business side, I lead financial planning, strategy, budgeting, and team leadership while also driving marketing efforts, from positioning and go-to-market planning to customer acquisition and B2B growth.

Optimize NLP Models with Backtracking for Text Summarization and More

Optimize NLP Models with Backtracking for Text Summarization and More

Master Multiple Linear Regression in Python with Scikit-learn and Statsmodels

Optimize GPU Memory in PyTorch: Boost Performance with Multi-GPU Techniques

Master Ridge Regression in Machine Learning: Combat Overfitting with Regularization

Alireza Pourmahdavi

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