In this blog, I want to talk a little bit about testing different fully connected neural networks to see how each performs on this dataset: https://www.kaggle.com/c/digit-recognizer this is a very famous and one of the first classical data sets in the computer vision field. we have images like this: and we want to be able to identify each image with least amount of errors. we have 42,000 labeled images for training we have 28,000 non-labeled for kaggle evaluation. In this blog I'll use few deep neural networks using Keras to build different models and evaluate them. each model will be different in either number of layers, nodes per layer to get a sense of what increases the accuracy of a neural network in this kind of problems and whether more layer & units and larger models mean better performance. I'll assume you are familiar with python, basics of neural network. as for Keras basics if you are not familiar with it, I recommend googling things along t
Blog about what I try or want to try.. while kids are sleeping !