Step by Step explanation of Observer Pattern in Practice — This is probably one of the most used design patterns in software development. If you feel unfamiliar with the name, maybe you might have heard its’ another name Publisher-Subscriber pattern. It almost can be seen everywhere in our daily apps, for example, emails, subscription news updates, real-time stock charts updates…

Hands-on Practice of Using Dataset Object — In the image recognition field, the general training set could take from GB to TB size, with each image growing bigger and bigger, there is no way to preload all images into the memory and do a model training. In this article, we will touch on how to make use…

CNN Explained — You probably have used convolutional functions from Tensorflow, Pytorch, Keras, or other deep learning frameworks. But in this article, I would like to implement the convolutional layers from scratch, which, I believe, could help one gains a deeper understanding of each component in the convolutional process. We are going to…

Breakdown the Fundamentals — In deep learning, generally, to approach the optimal value, gradient descent is applied to the weights, and optimization is achieved by running many many epochs with large datasets. The process is computationally intensive, and more than often you need to fit a dataset multiple times, thus, an efficient and fast…

Code it So You Understand it — In the last post, we have coded a deep dense neural network, but to have a better and more complete neural network, we would need it to be more robust and resistant to overfitting. The commonly applied method in a deep neural network, you might have heard, are regularization and…

Hands on Practice of Building a Deep Neural Network in Python — In last post, we’ve built a 1-hidden layer neural network with basic functions in python. To generalize and empower our network, in this post, we will build a n-layer neural network to do a binary classification task, in which n is customisable (it is recommended to go over my last…

Theory and Implementation Explained — One Hidden Layer NN We will build a shallow dense neural network with one hidden layer, and the following structure is used for illustration purpose. Before trying to understand this post, I strongly suggest you to go through my pervious implementation of logistic regression, as logistic regression can be seem as a 1-layer neural…

From Theory to Practice — Say we are doing a classic prediction task, where given a input vector with $n$ variables: And to predict 1 response variable $y$ (may be the sales of next year, the house price, etc.), the simplest form is to use a linear regression to do the prediction with the formula: