(Days 1 and 2 in folder)
Day 3 Reflection: I learned about linear regression, clustering, and preceptron.
Day 4 Reflection: I learned about tensorflow, pytorch, and keras
Day 5 Reflection: I learned about neural networks and how they work as well about different activation functions. I worked on learning the steps in a neural network and how forward pass and backward pass work.
Day 6 Reflection: I learned about Convolutional Neural Networks and its different parts such as feature extraction, pooling operationn, and RELU.
Day 7 Reflection: Today I learned about biases in machine learning including the causes, types, and what we can do to try and prevent bias in machine learning models. It was interesting to see an example of how a model becomes biased through the Survival of the Best Fit game simulation.
Day 8: I learned more about Convolutional Neural Networks including convolution layers, learned filters, dropout, and batch norm.
Day 9: I learned about loss functions and what types of loss functions to use depending on the problem. I also learned about optimizers to help minimize the loss function.
Day 10: I learned about activation functions and how to know when to use which activation function depending on the problem at hand.
Day 11: I learned about ethics in the area of machine learning and looked at ML algorithms in different areas.
Day 12: I learned about image classification and its strucutre as well as SVMs. I also learned some challenges that come with image classification.
Day 13: I learned about overfitting vs underfitting and how to reduce overfitting through methods like regularization.
Day 14: Today I learned about autoencoders, upsampling, and downsampling.
Day 15: I learned about affective computing and emotion AI.
Day 16: I learned about NLP and practiced tokenization, bag of words, stop words removal, lemmetization, etc.
Day 17: I learned about Computer Vision and worked with the Fashion MINST dataset to learn more about GANs.