This post is a continuation to my previous post Object Detection with Single Shot Multibox Detector. If you have not read the first part, I recommend you to read that first for a better understanding. In this post, along with some important concepts regarding training, I have talked about my observation of the model complexity […]

# Neural Network Demystified Part lll – Gradient Descent and Backpropagation

This post is the third and last part of the series Neural Network Demystified. If you haven’t read the first two parts please check Neural Network Demystified Part l – Building Blocks and Activation Functions and Neural Network Demystified Part lI – Deep Neural Network first. Training a Neural Network Neural networks are used for […]

# Neural Network Demystified Part ll – Deep Neural Network

This post is the second part of the series Neural Network Demystified. If you haven’t read the first part please check Neural Network Demystified Part I – Building Blocks and Activation Functions first. Artificial Neural Networks Artificial Neural Networks (ANNs) are motivated by the nervous system of the human brain where approximately 86 billion neurons are […]

# Neural Network Demystified Part I – Building Blocks and Activation Functions

Building Blocks of a Neural Network Although the original intention of designing a neural network was to simulate the human brain system to solve general learning problems in a principled way, in practice neural network algorithms work in much simpler way than a human brain. Current neural networks can be compared to statistical models having […]

# How deep is a Deep Neural Net?

As a machine learning researcher, I often get asked this question about “how deep a neural net is called a deep neural net”. After having some good reads and listening to some great AI specialists, I have finally come up with a satisfactory answer to this interesting but a bit tricky question. Before delving deep […]