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
Machine Learning Engineer Intern at PayPal: A Practical Guide
Landing a machine learning engineer intern position at PayPal is a fantastic opportunity. It’s a chance to work on real-world problems with massive datasets and impact millions of users. This article provides practical, actionable advice for aspiring ML engineers looking to secure an internship at PayPal.
Performance Engineer – Deep Learning: Practical Strategies for ML Optimization
As an ML engineer, I’ve seen firsthand how critical performance is in deep learning. Models that are brilliant in theory can fail in practice if they’re too slow, too resource-intensive, or prone to instability. This is where the “performance engineer – deep learning” role becomes
Deep Learning.AI: Your Practical Path to Mastering AI
Hi, I’m Alex Petrov, an ML engineer. If you’re looking to seriously upskill in machine learning and deep learning, you’ve likely come across DeepLearning.AI. It’s a platform founded by Andrew Ng, a name synonymous with accessible and high-quality AI education. This article isn’t about hype; it’s a
Understanding Reinforcement Learning and Agents: A Practical Guide for ML Engineers
As an ML engineer, I constantly look for ways to improve autonomous systems and decision-making processes. Reinforcement Learning (RL) stands out as a powerful paradigm for achieving this. It’s not just theoretical; the practical applications are immense, from robotics to personalized recommendations. This article
Neural Networks Verification with Coq: A Practical Guide
As machine learning models, particularly neural networks, become integrated into critical systems like autonomous vehicles, medical devices, and financial trading, the need for their reliability and safety is paramount. Traditional testing methods, while essential, often fall short of providing formal guarantees. This is where formal verification comes
Unleashing AI: Creating Neural Networks in Python and Scratch
Hi, I’m Alex Petrov, an ML engineer. Today, we’re going to demystify neural networks. You might think they’re complex, reserved for advanced programmers. But I’ll show you how to start creating neural networks in Python, and surprisingly, even visualize core concepts using Scratch. This article provides
Multi-Agent AI News: Practical Insights for Engineers and Businesses
As an ML engineer, I’m constantly tracking the practical applications and advancements in AI. Multi-agent AI, where multiple AI entities interact and collaborate (or compete) to achieve goals, is rapidly moving from academic research to real-world deployment. The recent multi-agent AI news highlights significant progress and
How NVIDIA’s Fundamentals of Deep Learning Course Assessment Works
As an ML engineer, I’ve seen firsthand the increasing demand for skilled professionals in deep learning. NVIDIA, a leader in GPU technology, offers a valuable course called “Fundamentals of Deep Learning.” Many aspiring engineers and data scientists wonder, “how is NVIDIA Funda Fundamentals of Deep Learning