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​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:

In another assignment, I worked on NLP Dataset: Sentiment Analysis dataset - 1.6 Million tweets. Please find dataset from here -

Smart Watch
NLP and DeepLearning works: Research
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