🗓 Since Python might be a good choice to start experimenting with Machine Learning I decided to learn its basic syntax first.In my spare time, as a hobby, I decided to dig into machine learning topics to make it less like magic and more like math to myself.
#Text to speech google docs mac software#
I'm a software engineer and for the last several years now I've been doing mostly frontend and backend programming. Therefore, sometimes you might see things like this:īut be patient, sometimes the model might get smarter 🤓 and give you this: Models might not perform well and there is a place for overfitting/underfitting.
This is rather a sandbox or a playground for learning and trying different machine learning approaches, algorithms and data-sets. ⚠️ First, let's set our expectations.️ The repository contains machine learning experiments and not a production ready, reusable, optimised and fine-tuned code and models. I've trained the models on Python using TensorFlow 2 with Keras support and then consumed them for a demo in a browser using React and JavaScript version of Tensorflow. ✊🖐✌️ Play with you in Rock-Paper-Scissors game.📸 Detect and recognize the objects you'll show to your camera.🖌 Recognize digits or sketches you draw in your browser.Each experiment consists of 🏋️ Jupyter/Colab notebook (to see how a model was trained) and 🎨 demo page (to see a model in action right in your browser).Īlthough the models may be a little dumb (remember, these are just experiments, not a production ready code), they will try to do their best to:
I've open-sourced new 🤖 Interactive Machine Learning Experiments project on GitHub.
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