Udacity Bertelsmann Scholar | Interested in Natural Language Processing, Deep Learning, Machine Learning
This was a 128 hours-course involving quizzes, assignments, midterm, and end sem examinations. In this course, Along with machine learning prerequisites, I have learned Deep feedforward networks, regularization, and optimization for deep models, Convolutional NeuralNet, Recurrent Nets, Autoencoders, Generative adversarial Nets (GAN).
In computer vision assignment, I worked on CIFAR-10- It dataset consists of 60000 32x32 colour images in 10 classes using google colab for Data Visualization and augmentation, used ResNet50 pretrained model, for Model Evaluation I used confusion matrix, precision, recall and two most incorrectly classified images for each class in the test dataset. For Hyperparameter Tuning I used Dropout and regularization, Please find your dataset from here: https://www.tensorflow.org/datasets/catalog/cifar10
In another assignment, I worked on NLP Dataset: Sentiment Analysis dataset - 1.6 Million tweets. Please find dataset from here - https://www.kaggle.com/kazanova/sentiment140