Still Waters Run Deep Learning

Before I get into the nitty-gritty of how I trained the neural network, I want to go over how I set up the environment for the training. There is quite a bit of detail involved, which I want to note down both as a reminder to myself, in case I ever need to repeat the process, and to show everyone what steps were necessary. See the last blog if you want to know which projects I considered ( In Which a Project was Undertaken, Part 1 :  link ). Maximum Power! I turn all my processing power up to 11 ( Wikipedia link , IMDB link ) At first I tried running the training on Google Colab, since I was used to doing so from the class assignments in the Coursera specializations  (see In Which Instructions were Sought, Part 1 : link , Part 2 : link , Part 3 : link for more details) . It was fine to run it in Colab back then, the models were small and most finished training very quickly, usually taking no more than 15~20 minutes to train a few hundred epochs, so there was no press...

With two Coursera specializations under my belt (which I detailed in In Which Instructions were Sought, Part 1 ( link ), Part 2 ( link ), and Part 3 ( link )), it's time to put theory into practice and make something new. What's the big idea? Go Big or Go Home? ( YouTube link ) I have been trying to come up with interesting projects for a while, and here is a list of some of the ideas that I think might be worth exploring: ______________________________________________________ Predicting the origin of a name: In today's modern global society, you often get the chance to meet people from faraway places. Sometimes you hear a name, and you can't place where the person is from. For example, typically Chinese and Korean names have three syllables, but to non-Koreans and non-Chinese, it might be difficult to tell them apart. A neural network can be trained to categorize the name, in essence a classification task with all the different origins. The difficulty i...

Digging Deeper He shovels well, he shovels VERY well. ( Wikipedia link , IMDB link ) As I mentioned in the last blog ( In Which Instructions were Sought, Part 2 , link ), I wanted to get more experience in some of the modern frameworks which are commonly used nowadays. The deep learning specialization covered the theoretical background well, and the assignments involved implementing the algorithms directly, which helped solidify understanding, but they did not go in depth into any of the frameworks, which means that in many production environments where frameworks are used to speed up implementation, I would have no relevant experience with the use of those frameworks. Having enjoyed the Coursera Deep Learning Specialization offered by deeplearning.ai, I wanted to take another course on the Coursera platform. I found two specializations which interested me: TensorFlow in Practice Specialization, offered by deeplearning.ai ( Coursera link )         Du...