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Intelligent Compilers: Machine Learning-Powered Compiler Autotuning
Abstract In this article, we look at how machine learning is boosting compiler autotuning. We provide a comprehensive overview of traditional compiler optimization techniques and their limitations, highlighting the need for more adaptive and data-driven approaches. Afterward, we dive deeper into various ML methods, including supervised and reinforcement learning, and...
Graph Neural Networks: Fraud Detection and Protein Function Prediction
Understanding AI applications in bio for machine learning engineers What do a network of financial transactions and a protein structure have in common? They’re both poorly modeled in Euclidean (x, y) space and require encoding complex, large, and heterogeneous graphs to truly grok. Graphs are the natural way to represent...
Optimizing Component Configuration in Extensive Software Projects
Here’s some proven methods for managing components in vast software ecosystems I recall a time when I was part of a massive refactoring initiative for a complex distributed system. At the time, our ultimate goal as a team was to streamline how various modules were organized, tracked, and deployed, but...
Don’t Be Afraid to Use Machine Learning for Simple Tasks
Many professionals view machine learning as advanced technology that you deploy when other approaches fail. In their mind, it’s a sophisticated tool reserved for problems too complex for conventional methods. Therefore, when I talk to businesses, I often hear comments like “We don’t need to use machine learning for that” or...
Intelligent Compilers: Machine Learning-Powered Compiler Autotuning
Abstract In this article, we look at how machine learning is boosting compiler autotuning. We provide a comprehensive overview of traditional compiler optimization techniques and their limitations, highlighting the need...
Graph Neural Networks: Fraud Detection and Protein Function Prediction
Understanding AI applications in bio for machine learning engineers What do a network of financial transactions and a protein structure have in common? They’re both poorly modeled in Euclidean (x,...
Optimizing Component Configuration in Extensive Software Projects
Here’s some proven methods for managing components in vast software ecosystems I recall a time when I was part of a massive refactoring initiative for a complex distributed system. At...
Don’t Be Afraid to Use Machine Learning for Simple Tasks
Many professionals view machine learning as advanced technology that you deploy when other approaches fail. In their mind, it’s a sophisticated tool reserved for problems too complex for conventional methods. Therefore,...
How to Succeed as a Machine Learning Engineer in the Industry
5 tips that helped me consistently exceed expectations in BigTech Have you ever wondered what it takes to succeed as a machine learning engineer? Do you struggle with defining your...
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Intelligent Compilers: Machine Learning-Powered Compiler Autotuning
Abstract In this article, we look at how machine learning is boosting compiler autotuning. We provide a comprehensive overview of traditional compiler optimization techniques and [more…]
Graph Neural Networks: Fraud Detection and Protein Function Prediction
Understanding AI applications in bio for machine learning engineers What do a network of financial transactions and a protein structure have in common? They’re both [more…]
Optimizing Component Configuration in Extensive Software Projects
Here’s some proven methods for managing components in vast software ecosystems I recall a time when I was part of a massive refactoring initiative for [more…]
Don’t Be Afraid to Use Machine Learning for Simple Tasks
Many professionals view machine learning as advanced technology that you deploy when other approaches fail. In their mind, it’s a sophisticated tool reserved for problems too [more…]
How to Succeed as a Machine Learning Engineer in the Industry
5 tips that helped me consistently exceed expectations in BigTech Have you ever wondered what it takes to succeed as a machine learning engineer? Do [more…]
A Visual Understanding of Neural Networks
The math behind neural networks visually explained Artificial neural networks are the most powerful and at the same time the most complicated machine learning models. [more…]
FANformer Is The New Game-Changing Architecture For LLMs
A deep dive into how FANFormer architecture works and what makes it so powerful compared to Transformer LLMs have always surprised us with their capabilities, [more…]
Mastering Temporal Data with Recurrent Neural Networks in Machine Learning
Embark on an exploration of Recurrent Neural Networks (RNN) and unlock the secrets to effectively processing sequential data in machine learning, opening doors to groundbreaking [more…]
Comparing Neural Networks to SVMs: Which is the Superior Machine Learning Algorithm?
The realm of machine learning is vast and the technologies within it, particularly neural networks (NNs) and support vector machines (SVMs), stand out for their [more…]