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Learning Data Science, ML models, Deep Learning, ANNs, CNNs nd RNNs through projects

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classification convolutional-neural-networks deep-learning deep-neural-networks machine-learning supervised-learning unsupervised-learning computer-vision

ml-projects's Introduction

ML-Projects

  • Data Visualization
  • Prediction of Confirmed cases of various countries (just basic implementation)
  • trained MNIST dataset on Neural Network with one hidden layer of 128 neurons
  • using Loss Function: sparse categorical crossentropy, optimizer: Adam
  • attaining an accuracy of ~98%
  • develop the neural network from scratch
  • Best accuracy of 80%
  • Image CLassification of Flowers
  • used concept of Transfer Learning
  • can attain accuracy of ~99%
  • worked on fashion_mnist dataset
  • implemented CNNs
  • can attain accuracy of ~92.8%
  • also tried to implement on CIFAR-10 dataset
  • can be deployed as application for fae verification
  • used Siamese network
  • used imdb reviews dataset
  • implemented RNNs from scratch (vocabulary intialization, embedding, RNN architecture, training & updating parameters, Prediction)

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