Hello folks! Are you happy or are you not sure? Alright, let's build a model that will help you find out if you're happy or not. Well, let's start with some basic understanding of this tutorial and later dive deeper into the neural networks. We're very well known what popular Computer Vision is. It is one of the most popular field of machine learning. Happiness Detection is also one of such field where we apply Computer Vision techniques. This is a binary classification type of problem where we'll building a model that will detect whether the input image is either smiling or not. The dataset is already labeled as smiling or not smiling. We'll be using 600 images for training and 150 images as test dataset. Before we get our hands into the core part, let's first import some libraries. Now let's know more about the data. After the execution, you'll be able to look at the number of data we've taken for training and testing the prepared model. N
This is part of 'Data structure and algorithm implementation in python' series of blog. Previous: Stack Next: Linked List Queue: ( source code ) Direct... First in first out discipline. Let's see in real life example: The man who is standing in front of a queue is served first. That man is in front of the queue because he entered that queue at first. Formally defining: A queue is an ordered collection of items from which items are deleted from the front of the queue and the insertion of item takes place from rear of the queue. the first one inserted will be the first element to be removed. Operations done in Queue: enqueue(item): This operation inserts an item at the rare of the queue i.e. if we are using python's list to implement queue then rare means last element in the stack and every insertions after an item inserted will be appended at last of the queue. dequeue(): This operation deletes item from the front end of the queue. If we are using python's list