In this article, you will learn.
Large deep neural networks or an ensemble of deep neural networks built for better accuracy are computationally expensive, resulting in longer inference times which may not be ideal for near-real-time inference required at the Edge.
The training requirements for a model are different from requirements at the inference. …
You are in a grocery store and scan the QR code on your favorite coffee. You can view the details of where the coffee beans grew, cleaned, triaged, processed, and then roasted. what-if you can trace the supply and distribution to know the means of transport and how long it took to arrive at the store and so forth
Is there a technology that can help record information at each step and make that information available to everyone transparently?
Welcome to the world of Blockchain
Blockchain is an immutable, distributed, decentralized; peer-to-peer ledger replicated across multiple nodes connected in a…
Particle Swarm Optimization(PSO) is inspired by nature. Flocks of bird and fish schools adjust their physical movement to avoid predators, seek food or mates, or optimize environmental parameters such as temperature.
Birds and fishes use swarm intelligence, social behavior, and movement dynamics to share information among their group for a greater survival advantage.
The PSO optimization objective is to find the optimum function that either maximizes the accuracy or precision or minimizes the loss.
The objective of PSO is to find a global optimum over a search space. A swarm in the algorithm is represented by a set of particles…
You will explore
My previous post was an Intuitive explanation of the Siamese Network, and in this post, it is the implementation of the Siamese network for Facial Recognition in TensorFlow.
Facial Recognition is to identify or verify people in the images or videos. It is the task to match faces to a given face or finding similar faces from a given facial dataset.
Siamese Network is a One-shot classifier with two mirror image subnetworks to rank similarity or dissimilarity between the two inputs using Similarity score.
You will learn what DBSCAN is, how it works, the pros and cons of DBSCAN, and finally, implementation.
DBSCAN is Density-Based Spatial Clustering for Applications with Noise, an unsupervised clustering algorithm which finds core sample of high density and expands cluster from them
You have a new account holder in your bank and would like to set his signature for verification. You have only one sample signature from the member. How can you use a neural network to perform the signature verification?
To find the answer, read on…
Siamese network inspired by Siamese twins has a unique architecture to naturally rank similarity or dissimilarity between inputs.
Learn what are AutoEncoders, how they work, their usage, and finally implement Autoencoders for anomaly detection.
AutoEncoder is a generative unsupervised deep learning algorithm used for reconstructing high-dimensional input data using a neural network with a narrow bottleneck layer in the middle which contains the latent representation of the input data.
Autoencoder consists of an Encoder and a Decoder.
This post is the second in the series; here you will explore One-Class SVM, a semi-supervised technique for anomaly detection.
Anomaly detection using Machine Learning can be divided into Supervised, Semi-Supervised, or Unsupervised algorithms.
In this post, you will explore supervised, semi-supervised, and unsupervised techniques for Anomaly detection like Interquartile range, Isolated forest, and Elliptic envelope for identifying anomalies in data.
You might have played “Find the odd one out” as a kid where you were given a few data points like Rose, Lily, Tulip, Daffodils, and Egg Plant, and then based on the data pattern, figured out that Egg Plant was the odd one out as all the remaining items are the name of flowers and Egg Plant is a vegetable.
Anomaly detection is very similar to finding the odd one out where…
Loves learning, sharing, and discovering myself. Passionate about Machine Learning and Deep Learning