VerisimilitudeX

VerisimilitudeX

0-day streak
I created a simple imputation software for epigenetic data (.bigwig) files! pt. 2
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I created a simple imputation software for epigenetic data (.bigwig) files!
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I coded my own Twitter classifier!
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I created a PCA model pt .2!
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I created a PCA model!
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I gathered 2 TB of data for my epigenetics imputation project!
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I made a KNN without using a library! pt. 2
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I made a KNN without using a library!
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I coded my own VAE for the imputation epigenetic data!
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I created my own neural network for Kaggle!
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I coded my own Neural Network that achieved an accuracy of >96% on Kaggle!
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I coded my own regression model from Schoolhouse.world!
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I finished my first Kaggle project!
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I finished training a CNN on the cifar 10 dataset!
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I trained my own machine learning model on the cifar 10 image dataset!
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I finished training my decision tree classifier!
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I created my first neural network!
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I finished neural network D and made this!
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I created 2 neural networks! github.com/VerisimilitudeX/openai_api_test/commits/main### Prob 3) NetC (15 points) This third neural network will be a convolutional neural network. It should use the following operations in this order: Use a convolution layer with kernel-width 5 and depth 25 (note: zero padding and stride = 1 are by default in `nn.Conv2d`) Use the ReLU activation function Use a max-pool operation with kernel-width 2 and stride 2 Reshape the image to a vector for the next step's input using .view(). * Use a fully-connected linear layer to the 10 outputs. This architecture can be seen visually in the following diagram (the left-most object is the input image). ![Network Architecture](courses.cs.washington.edu/courses/cse416/19su/files/assignment-resources/homework/hw7/nn.svg) Notice that these diagrams use the following notation to describe the dimensions of the results at each step.

channels @ height x width
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i created a diagram of a neural network!
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I trained and created my own first neural network
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I made a simple polynomial regression program using python, scikit learn, numpy, and pandas. it uses l1 and l2 regularization (elastic net) to reduce the size of the coefificents for each feature, which theoretically should reduce overfitting. This was my first machine learning project and I am very proud of it! github.com/VerisimilitudeX/predicthouseprice
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❄️ Day 3 of Hack Club's Winter Hardware Wonderland ❄️ For my #hardware-party project, I will build a smart plant watering system that uses a microcontroller and sensors to automatically water my plants based on soil moisture levels. I am excited to build this project because I love plants, but I often forget to water them. This system will help me take better care of my plants and save water by only watering when needed. I've been kinda busy this past week, so I didn't have much time to work on this, but I tested all of my hardware and also fully completed the solar power generator part that will allow me to use the watering system outdoors without having to worry about the battery running out or finding a power source.
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Having fun revising specific heat problems from chemistry class after getting first at National Science Bowl last year...time passes so quickly 😳
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Implemented error handling (username, password, profile picture, etc.) in the login system of Altruity, a revolutionary social media platform connecting philanthropists and charities, making it easy to discover and support causes that matter to you.
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Implemented error handling (username, password, profile picture, etc.) in the login system of Altruity, a revolutionary social media platform connecting philanthropists and charities, making it easy to discover and support causes that matter to you.
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Learning how to use PocketBase - it's a really cool way to link your database with your frontend and Go backend!
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Working on developing my nonprofit's website with some friends!
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