Learning how to use TensorFlow and selecting data!

Last week, I researched and learned about using TensorFlow, Google’s software for machine learning, and one of the most important concepts, training and testing data of a model. Firstly, I was able to try out TensorFlow while continuing my Google Machine Learning Crash Course to really see how the software works and the types of libraries (Numpy and Pandas) I would use for machine learning projects. I got to learn quite a lot by doing the Collab exercises, another Google software, in my crash course as they gave me the opportunity to experiment with different parameters such as the learning rate of a model, batch size, and the number of epochs that a model should do. Furthermore, I learned how to select data for training, testing, and for validation (to make sure that the model does not overfit the training data). I have now gained a perspective over the basics of training and testing models and aspire to learn more about the complicated parts of machine learning. I still continue to wonder how I can create my own model and write specific algorithms.

My goal for this new week would be to learn more about machine learning theory specifically about some more complex algorithms and move on from linear correlation algorithms. I continue to strive to learn about more complex subjects and the basics of machine learning through the crash course that I am currently taking. I also hope to start moving from the basics and theory to more of the implementation in the next few weeks. So see you next week, same place, same time.