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I took part on a project tentatively named “Mini Escape Maker” - an educational tool that lets teachers create interactive mystery games for their students. I focused mainly on the front-end implementation. Project Overview It’s a tool targeted towards educators to help build interactive mystery games (in the style of escape/clue games). The idea is you start with some mystery story where there’s an array of suspects. Educators then create pairings of clues and tools, and students can match them up in order to produce findings. ...

Neural Networks See Machine Learning How do you decide how many neurons to use per layer? One way is to start with all layers uing the same amount of neurons and continue adding them until they start overfitting the data Dropout: Regularlization technique to avoid overfitting. Leaves out data to better deal with general cases. 20%-50% dropout is a good starting range. Momentum: Helps finding the direction of next descent and prevent oscillations. Typical choice between 0.5 to 0.9 Epochs #times the whole training data is shown to netowrk during training. 1 epoch = one forward and one backward pass of all training data Back Propagation See Machine Learning ...

I’ve always had trouble understanding probability, especially when entering into the more theoretical aspects of it. Here, I want to cover some of the basic concepts and functions core to probability in an easily digestible format that I can refer to later on when I inevitably forget it all. Random Variable Whenever there’s a question of probability, you tend to have some range of possible outcomes sourced from a specific event. ...

In preparation for a deep learning course I’m taking over the Summer, I’m taking a short intro course on machine learning to help prepare me for some of the fundamental concepts. I’ve been avoiding AI for a while but given its ongoing application in nearly everything now, I figure it’s more than worth getting my feet wet. ML Overview flowchart LR subgraph Shader Lifecycle direction LR d[("Dataset")] --> m(("Model")) --> o("Predicted Values") ---|compare with| a("Actual Values") -->|calculate| l["Loss"] l -->|training data| m end Loss Functions When we compare our predicted results to the actual results, how do we calculate the loss? ...