All you need know how train scalable and efficient deep learning models Table Of Contents : (i) Introduction (ii) Motivation (iii) Model Training without pruning (iv) Model Training with pruning (v) Comparison of both model sizes (vi) Comparison of both model performances (vii) Comparison of both model accuracy (viii) Conclusion …

All you need to know impact of Machine Learning on Unstructured data Table Of Contents : (i) Problem Statement (ii) Motivation (iii) Data Processing (iv) Data Cleaning (v) Machine Learning Model Building (vi) Deep Learning Model building (vii) Deployment (viii) Conclusions (ix) Future Scope (x) References (i) Problem Statement : Our objective is…

All you need to know how useful machine learning in real-world Table Of Contents: (i) Problem Statement (ii) Motivation (iii) Data Processing (iv) Data Augmentation (v) CNN from Scratch (vi) InceptionV3 (vii) Deployment with Flask (viii) Conclusion (ix) Future Scope (x) References (i) Problem Statement : Our problem statement is Classifying the given Cotton Plant Or…

All you need to know condition’s for Naive Bayes This Article main Agenda is to discuss boundary cases of Naive Bayes. I hope you know the theoretical concept of Naive Bayes. Boundary Case 1: Problem Statement First, we need to check the problem statement of the Dataset Because Naive Bayes was originally intended to be…

Clustering Algorithms with Hyperparameter optimization Table of contents : (i) Article Agenda (ii) Data Processing (iii) K-mean Clustering with Hyperparameter Optimization (iv) Hierarchical Clustering (v) DBSCAN Clustering with Hyperparameter Optimization (vi) Conclusion (vii) References (i) Article Agenda : This article is purely related to the implementation of Clustering Algorithms on any data set. …

Performance metrics of Classification models

A theoretical way to understand which model is powerful. VC dimensions are used to quantify how powerful is the model For example, if we have a pre-processed data set and we have Linear regression, SVM, Ensemble models, Neural Network models. In a real-world(Practical) what we do is apply one by…

All you want to know about why pre-padding requires While learning the architecture of LSTM we are all focused on the Forgot gate, Input gate, and Output gate. But when comes to implementing part before applying Embedding layer we apply pre-padding on Input data( Sequence of words) what we think: …

Understanding Deep Learning Algorithms Architecture With Feynman Technique If you want to learn anything first ask yourself why should I learn this, that question gives you the motivation to learn Feynman was an American theoretical physicist his thought process on learning is if you learn any new concept if you…