Pinned Repositories
Adaptive-Linear-Neuron
Adaline was published by Bernard Widrow and his doctoral student Tedd Hoff and can be considered as an improvement on the perceptron algorithm. This algorithm illustrates the key concepts of defining and minimizing the continuous cost function that lays the groundwork for understanding more advanced machine learning algorithms for classification, such as logistic regression, support vector machines and regression models
amazon-clone
Amazona
aryalp2.github.io
basobas-repo
blogapi
bookworm-react
Source code for free live-coding series on YouTube
Clustering-Analysis
Cluster analysis, a category of unsupervised learning techniques that allows us to discover hidden structures in data where we do not know the right answer upfront
coding-interview-university
A complete computer science study plan to become a software engineer.
Dimensionality-Reduction
Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA)
aryalp2's Repositories
aryalp2/aryalp2.github.io
aryalp2/Dimensionality-Reduction
Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA)
aryalp2/Adaptive-Linear-Neuron
Adaline was published by Bernard Widrow and his doctoral student Tedd Hoff and can be considered as an improvement on the perceptron algorithm. This algorithm illustrates the key concepts of defining and minimizing the continuous cost function that lays the groundwork for understanding more advanced machine learning algorithms for classification, such as logistic regression, support vector machines and regression models
aryalp2/amazon-clone
aryalp2/Amazona
aryalp2/basobas-repo
aryalp2/blogapi
aryalp2/bookworm-react
Source code for free live-coding series on YouTube
aryalp2/Clustering-Analysis
Cluster analysis, a category of unsupervised learning techniques that allows us to discover hidden structures in data where we do not know the right answer upfront
aryalp2/coding-interview-university
A complete computer science study plan to become a software engineer.
aryalp2/Covid-Tracker
aryalp2/cs-video-courses
List of Computer Science courses with video lectures.
aryalp2/Data-Preprocessing
The quality of the data and the amount of useful information that it contains are key factors that determine how well a machine learning algorithm can learn. Therefore, it is absolutely critical that we make sure to examine and preprocess a dataset before we feed it to a learning algorithm. In this chapter, we will discuss the essential data preprocessing techniques that will help us build good machine learning models.
aryalp2/devconnector_2.0
Social network for developers, built on the MERN stack
aryalp2/developerSocialNetwork
aryalp2/Ensemble-Learning
These are techniques and explore different methods for constructing a set of classifiers that can often have a better predictive performance than any of its individual members.
aryalp2/firebase-react
aryalp2/FYP-Content-Management-System
MERN Stack Content Management System with Strapi
aryalp2/Hyperparameter-tuning
The best practices of building good machine learning models is by fine-tuning the algorithms and evaluating the model's performance!
aryalp2/Machine-Learning-Classifiers-Using-Scikit-Learn
A tour through strong machine learning algorithm that are used in academia and industry.
aryalp2/mathewcarpenter
aryalp2/Multilayer-Perceptron
aryalp2/Projects
:page_with_curl: A list of practical projects that anyone can solve in any programming language.
aryalp2/react-blog
aryalp2/Regression-Analysis
Regression models are used to predict target variables on a continuous scale.
aryalp2/Sentiment-Analysis
The subfield of Natural Language Processing (NLP) called sentiment analysis is concerned with analyzing the polarity of documents and learn how to use machine learning algorithms to classify documents based on their polarity: the attitude of the writer.
aryalp2/Stochastic-Gradient-Descent
Imagine we have a very large dataset with millions of data points, which is common in machine learning applications. Running batch gradient descent can be computationally quite costly in such scenarios since we need to reevaluate the whole training dataset each time we take one step towards the global minimum. A popular alternative to the batch gradient descent algorithm is stochastic gradient descent, sometimes also called iterative or online gradient descent. Instead of updating the weights based on the sum of the accumulated errors over all samples.
aryalp2/tiktok-clone
aryalp2/todo-list
aryalp2/whatsapp-clone