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Precision & Recall (aka Sensitivity) On classification models for ML, I still confuse precision for recall and vice versa. So it was good today that I came across this handy chart that illustrates the differences. A good precision score is really important in areas where getting a false positive is
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I’m sceptical about “prompt engineering” being a deep specialised thing. It felt a bit like gimmicky hype of a new job profession that would ride the wave of generative AI hype. Why? Because writing LLM prompts isn’t exact an science or precise engineering (more on this here.) However,
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AI is going to displace lots of jobs. So should we all just retrain away from our non-AI work and specialise to work exclusively on AI? I completed the “AI for Everyone” course today, and near the end, AI expert Andrew Ng shared a story that will be relevant to
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Experiment first. Strategy comes after. In today’s “AI for Everyone” course material, a key highlight was an insight from Andrew Ng. He’s worked on tons of AI projects at companies like Google, Baidu, and other clients in traditional industry. Usually, people want to dive right into strategy before
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In today’s “AI for Everyone” video, I went through a section on a framework you can use to figure out what AI projects are worth pursuing in a business (rather than research) setting. The framework is simple. Before taking on a project, you work to answer two questions: (1)
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Struggled to get in time to read up on AI today but watched a few videos on the train from the “AI for Everyone” course. Key takeaways: * The workflow of every good machine learning project has 3 broad steps: * (1) Collect the data – Not all data is created equal. You
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I continued the “AI for Everyone” course today. Key takeaways: * Deep learning, a subset of machine learning, is synonymous with neural networks (some shallow neural nets aren’t strictly ‘deep learning’, but broadly speaking, ‘deep learning’ is the catch-all term people have settled with for neural networks). * ML works well
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I shared a post today about a team of agents I built to do research work. More about this here. This evening I also decided to return to the absolute basics of AI, and have started another online course. “AI for Everyone” is a non-technical introduction to AI by the
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I spent a big chunk of today learning all about AI agents. Here’s how researchers describe them: “As opposed to zero-shot prompting of a large language model where a user types into an open-ended text field and gets a result without additional input, agents allow for more complex interaction
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I was finally able to train a classification model using Google’s Vertex AI. When I do this locally on my machine with the same training data it trains in a few seconds. On Vertex AI it took 1 hour 46 minutes! You can see the training budget I set
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I spent today catching up on AI news, and reading about Meta’s new open-weights model Llama 3. Note the use of the word “open-weights” rather than “open-source”, since Llama 3 has some restrictions on use. This is an impressive release by Meta. They trained the model on 24,000
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Today I decided to try out Google’s Vertex AI — a machine learning platform that makes it easier to train and deploy ML models. In the last few weeks I’ve been writing code in Python to train models, but Vertex AI provides a low-code platform and a set of