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
Posts
Not comparing with other's But there are nice and great tutorial on data structure and algorithm implementation in python. But in this series of blog we will be more focused on academic requirement and algorithm based implementation. So Alert!! there will be some cheesy theory on related topics. In these series of blogs we will try to cover some examples of data structures with it's algorithm, theory and implementation in python. We will dive into the following topics Stack Queue Linked List (singly, doubly, circular) Hash Table Heap Data Structure Binary Tree After one or may be two topics covered we will switch our session and continue with another post. Why one should learn data structure and algorithm? The very first answer is it improves your problem solving skill. Learning Data structure and algorithm shows you how the coding follows algorithm. whenever you develop a program you have to handle various data and data structure storage format to those data and organizing all
Region Based Convolutional Neural Networks: How you can achieve your own object detector with transfer learning.
In this article we will try to cover the R-CNN object detector implementation and how transfer learning could h elp in to classify object. We are going to build a raccoon detector using R-CNN by classifying a raccoon and no raccoon from the raccoon dataset by dat train using transfer learning with fine tuning approach (another approach is feature extraction). The reason I've choose the raccoon dataset is that it has a nice annotation files for every faces of raccoon in the images and many more research has gone through this dataset for object detection. Before Starting: This article is going to be a bit long so I highly recommend you to s3et yourself free to read it and actually implement it. In this tutorial we will be using selective search algorithm to find ROIs (Region of Interest), boundaries and also be using transfer learning. To read and learn these I recommend the series of objection detection blog and theany line you didn’t get tutorials from pyimagesearch by