“Many strategies used in machine learning are explicitly designed to reduce the test error, possibly at the expense of increased training error. These strategies are collectively known as regularization.” — Goodfellow et al.

In Neural Networks, obtaining a generalized model by choosing the right set of parameters(Weights and Biases) is…


Recently, I was reading about NFNets, a state-of-the-art algorithm in image classification without Normalization by Deepmind. Understanding the functionality of Batch-Normalization in Deep Neural Networks and it effects was one the key element to understand NFNets Algorithm. Just thought of sharing my understanding of Batch-Normalization.

Training Deep Neural Network with…


Gradient Descent, a first order optimization used to learn the weights of classifier. However, this implementation of gradient descent will be computationally slow to reach the global minima. If you want to read and understand about gradient descent read my previous article on “Understanding Gradient Descent”.

Stochastic Gradient Descent(SGD) is…


R-CNN is a region based Object Detection Algorithm developed by Girshick et al., from UC Berkeley in 2014. Before jumping into the algorithm lets try to understand what object detection actually means and how it differs from image classification.


We have been studying matrix operations like Ax = b from schooling, but have we ever realized how its simplified linear regression (y=mx+c) problem. Yes, Today we are going to use matrix operations to find slope(m) and intercept(c) for predicting the car resales value.

Before getting into problem let us…


If you are dropped in some part of Himalayas around 8 AM, How will we think to get to the safe place before sunset ? — Gradient Descent is the actual solution behind your thought process. …


On 27 December,2020, I was travelling to my native town for my vacation. During the travel we crossed 4 toll plazas and wondered how FASTag works. And as a coincidence in todays news Indian Government has mandated the use of FASTag from January 1 for all vehicles to pass through…


During my early 2000's my father used to drive me to my home town for vacation. which will take approximately 8 hours from were I live. During our journey my primary role is to sit beside him and wake him up whenever his eyes getting closed. What if I am…


Why Loss function is more important in Machine Learning Applications? In my last article we discussed about parameterized learning, such types of learnings will take some data as input and class labels. …


Parameterized Learning is a learning model which can summarize the data with a set of parameters of fixed size. No matter how much data you throw at the parametric Model, it wont change its mind about how many parameters it needs. — Russel and Norvig(2009)

Parameterized Model includes four major components — includes Data, Scoring , Loss Function and Weights and Biases. We are going to see each components with a practical example.

Data

Data will be our input…

Ramji Balasubramanian

Machine Learning Engineer

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