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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
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SVM, Precision & Recall. I completed the final honors exercise today. Here’s a confusion matrix of my support vector machine model model. This chart is read diagonally left to right, and then diagonally right to left. In other words, we start with the diagonal top left to bottom right:
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I’m almost done with the honors assignments and today I went through a logistic regression model exercise. The code is on Github. I used ChatGPT to help me write out the code for the visualizations. Here’s one I found particularly helpful. It shows the top 10 coefficients (aka
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Today I trained a decision tree model on weather data from the honors lab I’m completing. The model achieves a 83% accuracy on predicting if it will rain or not, using historical data. The chart below shows the decision tree steps. More performance metrics can be viewed below. I’