wanderinghominid Goto Github PK

annotated_deep_learning_paper_implementations

🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cyclegan, stylegan2), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, etc. 🧠

face-mask-detection

Detecting face masks using Python, Keras, OpenCV on real video streams

mlproject-churnprediction

multi-camera-live-object-tracking

Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.

my_first_rails_app

This is my first rails app.

pygad

sample_data_sets

sample data

test

test repo

validation-accuracy-and-loss-gap-minimalization-on-a-cnn-model-using-keras-with-adam-optimizer

Abstract— The Artificial Neural Networks (ANN) are complex systems that is patterned from the human brains. ANN are given input parameters for modelling and training in solving nonlinear problems in order to accurately predict the desired output values. The Convolutional Neural Network (CNN) is an Artificial Neural Network wherein it was designed to learn and analyze digital images by using multiple segments such as feature learning and image classification. This article uses CNN in order to classify files with mask and no-mask images by obtaining the optimal number of epochs that has a small gap between validation accuracy and loss using Adam optimizer.

wanderinghominid

Config files for my GitHub profile.