In Live 90, we connected via SSH to a Raspberry Pi and took some photos with the Pi Camera, and then trained YOLOv7 on a dataset of hand sketches and detected drawings, text, and arrows from several pages of one of my sketchbooks.
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In Live 89, we saw an overview of TensorFlow Signatures and did a hands-on demo to implement them as well as to understand Python decorators.
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In Live 88, we worked on the Note Parser side project, worked through TipTap's documentation to create custom extensions, and did a live Getting Simple Q&A.
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In Live 87, we saw an overview of OpenAI's Image API, which lets you interact with DALL-E 2 programmatically to generate and edit images and request image variations. You can go to OpenAI's API to sign up, read the documentation, and generate API keys.
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I recently got access to OpenAI's DALL·E 21 text-to-image beta. In short, this AI system can generate images from text prompts, create semantic local edits by selecting an image region and altering your prompt text, or generate variations of uploaded or generated images.
I've been playing with it and will be doing a live demo today in Live 79 and sharing some of my experiments and thoughts on this tool, which is quite impressive.
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In Live 70—I learned about YouTube's Upload Python API, which allows us to programmatically upload high-resolution videos to YouTube from a client application or the command-line interface.
Among other things, we learned how to create a Google Cloud Platform project and create API keys and OAuth 2.0 credentials to authenticate custom applications to different Google APIs. We also saw how to restrict your client id and secret to a specific IP address during testing and how to scope our application to certain API calls (say, uploading videos to YouTube or changing video thumbnails).
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In yesterday's live stream—Live 68—we took a look at the linear regression following Aurélien Géron's Hands-On Machine Learning book. We saw the normal equation, how to calculate the dot product of two matrices and the inverse of a matrix, and saw a few different ways in which, with code, you can solve a linear equation, plus how to do it with Sci-Kit Learn's LinearRegression class.
You can take a look at the Linear Regression Colab notebook.
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