The Rise of Artificial Intelligence

Hey Readers! Welcome to yet another post where I play with a self designed neural network. This CNN would be tackling a variant of classical MNIST known as Fashion MNIST dataset  . Before we start exploring what is the approach for this dataset, let's first checkout what this dataset really is. Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. ...

Hey there! My dear readers.  Today this kernel review is going to be about the famous MNIST data-set, one of the most famous introductory datasets which we encounter along side Iris dataset and the titanic survival challenge data sets.  Since it is a competition kernel, I have decided not to make it public. (yet) Also,  if you want to try your hands at the challenge itself, then you can find the challenge page here :  Note: This kernel has been largely focused on network modelling rather than Exploratory Data Analysis because it's simple, classic stuff. Still, I will try my best to explain that stuff here. Exploratory Data Analysis First indication of a great dataset is the face that it gives all the mentioned labels equal rows in it and this one doesn't disappoint. This data set maintains a fair 4000+ entries per label which actually is a great statistic for a good dataset. And the next thing one needs to know is how...

Data Science is reaching new levels and so are the models. But reaching a whooping 99.24% accuracy using simple feature engineering and a simple Decision Tree Classifier ? That's new! Hello everyone, today I am going to present you my model which can predict value range of a tender in Seattle Trade Permits with a whooping accuracy of 99.24 % (With some obvious caveats which I will discuss in the end). My Kernel : Yet Another Value Prediction The Prediction Kernel BASIC EDA This time out, I am going to use plotly library in Python. This is literally the best option for interactive plots and if you actually visit the kernel, you will understand why. First of all, we will focus on checking out the Top Grossing Contractors in the Seattle area who have earned the most out of the tender acquisitions. This will lead to this interactive graph: Similarly, one could plot out another graph for Amount earned per project. But another thing which caug...