Posts tagged Machine Learning

With transfer learning, we can take a pretrained model, which was trained on a large readily available dataset (trained on a completely different task, with the same input but different output). Then try to find layers which output reusable features. We use the output of that layer as input features to train a much smaller network that requires a smaller number of parameters. This smaller network only needs to learn the relations for your specific problem having already learnt about patterns in the data from the pretrained model. This way a model trained to detect Cats can be reused to Reproduce the work of Van Gogh

via https://medium.com/nanonets/nanonets-how-to-use-deep-learning-when-you-have-limited-data-f68c0b512cab

This last year I’ve been getting back into machine learning and AI, rediscovering the things that drew me to it in the first place. I’m still in the “learning” and “small studies” phase that naturally precedes crafting any new artwork, and I wanted to share some of that process here. This is a fairly linear record of my path, but my hope is that this post is modular enough that anyone interested in a specific part can skip ahead and find something that gets them excited, too. I’ll cover some experiments with these general topics: Convolutional Neural Networks, Recurrent Neural Networks, Dimensionality Reduction and Visualization, Autoencoders

via https://medium.com/@kcimc/a-return-to-machine-learning–2de3728558eb