Unimol Fine-Tuning: Practical Guide for Better Molecular Understanding
As an ML engineer, I’ve seen firsthand the power of pre-trained models. In drug discovery and materials science, molecular modeling is critical. Unimol, a powerful pre-trained molecular representation model, offers a significant leap forward. However, its true potential is unlocked through fine-tuning. This article provides a practical,
Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning – A Practical Guide by Alex Petrov
As an ML engineer, I’ve spent a lot of time wrestling with vision models. They’re powerful, no doubt, but often fall short when it comes to true “reasoning.” We can train a model to identify objects, segment images, or even generate captions,
LISA: Reasoning Segmentation via Large Language Model – A Practical Guide for ML Engineers
As an ML engineer, I’m always looking for ways to bridge the gap between high-level understanding and pixel-perfect execution in computer vision. Traditional segmentation models, while powerful, often lack the contextual reasoning that humans inherently possess. This is where **LISA: reasoning
Enhancing Large Language Models with Reliable Knowledge Graphs: A Practical Guide by Alex Petrov
As an ML engineer, I’ve spent significant time working with large language models (LLMs). While incredibly powerful, LLMs often face challenges with factual accuracy, hallucination, and providing up-to-date information. They learn from vast datasets but lack a structured understanding of the
Understanding and Mitigating Bias in Convolutional Neural Networks
As an ML engineer, I’ve seen firsthand how powerful Convolutional Neural Networks (CNNs) are. They drive image recognition, medical diagnostics, and autonomous vehicles. But their widespread adoption also means we need to talk about a critical issue: bias. A bias convolutional neural network isn’t just an academic
AI Automation: Build LLM Apps – Practical Guide for Engineers
Hey everyone, Alex Petrov here. I’m an ML engineer, and I’ve been building with Large Language Models (LLMs) since their early days. The hype is real, but so is the potential for practical, impactful AI automation. This guide is for engineers who want to move
Grounded Reinforcement Learning for Visual Reasoning: Practical Applications and Implementation
As an ML engineer, I’ve spent a significant amount of time working with systems that need to understand and interact with the visual world. Traditional computer vision excels at classification and detection. However, true visual reasoning, the ability to understand *why* something is happening, predict
Cracking the Meta Machine Learning Engineer Intern Role: Your Actionable Guide
Landing a machine learning engineer intern position at a company like Meta is a highly competitive endeavor. This isn’t about being a “rockstar” or having a “unicorn” profile. It’s about demonstrating practical skills, a solid understanding of fundamentals, and a genuine passion for building
MemoryLLM: Towards Self-Updatable Large Language Models
Hello, I’m Alex Petrov, an ML engineer. I spend a lot of time thinking about how we can make large language models (LLMs) smarter and more adaptable. One of the biggest challenges we face with current LLMs is their static nature. Once trained, they don’t inherently learn new information
Crafting Your Convolutional Neural Network Logo: A Practical Guide
As an ML engineer, I’ve seen countless projects, from the simplest scripts to complex production systems. One small but often overlooked detail is the project’s visual identity, particularly the logo. For something as fundamental as a Convolutional Neural Network (CNN), a well-designed logo can significantly impact