In this article, you will gain an understanding of
K-Means clustering is a simple, popular yet powerful unsupervised machine learning algorithm. An iterative algorithm to finds groups of data with similar characteristics for an unlabeled data set into clusters.
The K-Means algorithm aims to have cohesive clusters based on the defined number of clusters, K. …
You will learn
You trained an ML model with great performance metrics and then deployed it in production. The model worked great in production for some time, but your users observed the model recently is not predicting reliable results.
What must be going on with the model? Is it the model that is the issue or the data in the production that is the root cause?
We provide the data and the results during the training of a Machine Learning/Deep Learning…
In this article, you will explore common techniques like KL Divergence, Jensen-Shannon divergence, and KS test used in Machine Learning to measure the similarity between distributions statistically.
Why do we need to measure similarity or divergence between probability distribution used in Machine Learning?
In Machine Learning, you will encounter probability distributions for continuous and discrete input data, outputs from models, and error calculation between the actual and the predicted output.
This article is an experimental work to check if Deep Convolutional Autoencoders could be used for image anomaly detection on MNIST and Fashion MNIST.
Functionality: Autoencoders encode the input to identify important latent feature representation. It then decodes the latent features to reconstruct output values identical to the input values.
Objective: Autoencoder’s objective is to minimize reconstruction error between the input and output. This helps autoencoders to learn important features present in the data.
Architecture: Autoencoders consists of an Encoder network and a Decoder network. The encoder encodes the high dimension input into a lower-dimensional latent representation also referred to…
We are shaped by our thoughts; we become what we think- Gautam Buddha
Introspection on my mindset and thought process helped me change my destiny.
Around 6 years ago, I was working with a mindset that my abilities were fixed, my learning was limited, and as a result, my growth was stagnant. I was not happy with my then-current state.
Words of the great philosopher echoed.
Knowing yourself is the beginning of all wisdom-Aristotle
When I was looking outside, I believed that someone else was responsible for my situation. I was not happy or peaceful. Introspection helped me understand that…
Object detector models are composed of
The backbone of the Object detector can be pre-trained neural network.
Example: ImageNet, VGG16 , ResNet-50 , SpineNet , EfficientNet-B0/B7, CSPResNeXt50 or, CSPDarknet53 or ShuffleNet running on CPU.
Object detector models insert additional layers between the backbone and head…
What will you learn?
Generative Adversarial Network consists of two deep neural networks: Generator and Discriminator
Generator: Its objective is to learn the data distribution from the training data to produce images that resemble the training data.
Discriminator: A binary classifier whose objective is to distinguish the real training data from the fake data…
This post will learn to create a DCGAN using PyTorch on the MNIST dataset.
GANs were invented by Ian Goodfellow in 2014 and first described in the paper Generative Adversarial Nets.
GAN is Generative Adversarial Network is a generative model to create new data instances that resemble the training data set. GAN is implemented using two neural networks: Generator and Discriminator
The Generator’s objective is to learn the data distribution for the training data to produce fake images that resemble the training data.
The Discriminator is…
What will you learn here?
This article is adapted and inspired from Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
What is the difference between Supervised Learning, Unsupervised Learning, and Reinforcement Learning?
Supervised Learning algorithms learn from a labeled dataset. The labels in the dataset provide the answer to the input data.
The Supervised algorithm's objective is to find the function(f)…
Here you will learn to create a CNN model with similar architecture using Keras and PyTorch on MNIST dataset
Loves learning, sharing, and discovering myself. Passionate about Machine Learning and Deep Learning