As a full-stack developer and coder, I spend my days writing instructions for computers to follow. Computers are deterministic systems – given the same input, you always get the same output. So it might seem strange for a programmer to ponder the probability of an event like tomorrow‘s sunrise. But as mathematician Pierre-Simon Laplace demonstrated,…
Machine learning (ML) has quickly become one of the most disruptive and transformative technologies of the 21st century. From automating complex business processes to delivering personalized customer experiences, ML is enabling organizations across industries to innovate faster, make smarter decisions, and create new forms of value. According to Deloitte‘s 2020 State of AI in the…
Machine learning is eating the world. From recommendation systems to self-driving cars, ML models power many of today‘s most innovative applications. As a full-stack developer diving into this exciting field, one of the first questions you‘ll face is: How do I choose the right machine learning algorithm for my problem? The answer, as with most…
As a machine learning engineer, you know that one of the most fundamental assumptions for supervised learning is that the training data used to build models comes from the same distribution as the test data the models will be evaluated on. More formally, we assume that training examples $(x^{(i)}, y^{(i)})$ are drawn i.i.d. (independently and…
Automatically generating summaries of long text documents is a challenging natural language processing task. But how do we know if a machine-generated summary is actually any good? That‘s where ROUGE comes in. ROUGE, which stands for Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics for evaluating both automatic summarization and machine translation of…
As a seasoned full-stack developer who has worked on many machine learning projects over the years, I‘ve frequently encountered the issue of overfitting. It‘s an insidious problem that can sneak up on you if you‘re not careful, leading to ML models that seem to perform amazingly well in training but then fail miserably when deployed…
Machine Learning (ML) is becoming an increasingly critical capability for modern software applications, enabling powerful features like personalization, prediction, and automation. However, as more organizations look to adopt ML at scale, they are realizing that developing and deploying production ML systems introduces new challenges compared to traditional software. This is where MLOps comes in –…
Machine learning (ML) is one of the hottest and most impactful technologies today. It powers everything from the personalized product recommendations you see on Amazon to the virtual assistants in our smartphones to the self-driving cars poised to revolutionize transportation. But what exactly is machine learning? How does it work? And what do you need…
Neural networks are taking the world by storm. From powering self-driving cars and facial recognition to generating eerily realistic deepfakes and carrying on human-like conversations, these biologically-inspired algorithms are behind many of the most transformative advances in artificial intelligence over the past decade. As a web developer, you might be wondering how neural networks fit…
Image segmentation is a fundamental task in computer vision with wide-ranging applications, from autonomous driving and medical diagnosis to augmented reality and creative tools. However, building high-quality segmentation models has traditionally required massive labeled datasets, which are time-consuming and expensive to create. The Segment Anything Model (SAM), developed by Meta AI and released in March…