# Face Recognition with Deep Learning

Hello, all! I hope you got excited by the title itself. What if I tell you that building a face recognition system is not so difficult? Yes, it is, and of course very exciting. Let’s build a complete face recognition system which enables you to enroll a new candidate into the system and perform recognition with higher accuracy!

# Building a Reverse Image Search Engine

Ever wondered, how does the Google reverse image search engine works which take in an image and returns you the most similar images in a fraction of a second? How does the Pinterest let you search the visually similar images of the selected objects? Sounds interesting? Do you want to understand and build similar kind of a system? If yes then you are at the right place. Continue reading

# Implementing a simple RNN

In this post we will be implementing two simple Recurrent Neural Networks (RNN) one for classification and the other for regression tasks.

### Classification using RNN

It takes in a binary number and returns the XOR of the number. For example if the input is 10110 then it should output 11011, as Continue reading

# Create your own Object Detector

Creating a custom object detector was a challenge, but not now. There are many approaches for handling object detection. Of all, Haarcascades and HOG+SVM are very popular and best known for their performance. Though Haarcascades which were introduced by Viola and Jones are good in achieving decent accuracy, HOG+SVM proved to outperform the Haarcascades implementation. Here in this post we are going to build a object detector using HOG+SVM model. The output from our detector is something similar as shown below.