Multi-Layer Perceptron and Backpropagation | Deep Learning
An MLP is composed of one (passthrough) input layer, one or more layers of TLUs, called hidden layers, and one final layer of TLUs called the output layer (seeFigure below).…
Save the Prisoner Program in Python | Hacker Rank Solution
A jail has a number of prisoners and a number of treats to pass out to them. Their jailer decides the fairest way to divide the treats is to seat…
Find Kth Number Program in Python
Introduction: The task at hand is to implement a Python function that finds the kth lexicographically smallest integer in the range from 1 to n. To achieve this, we will…
SQL ADVANCE | The TOP Clause
SELECT TOP number|percent column_name(s) FROM table_name SQL SELECT TOP Equivalent in MySQL and Oracle: MySQL Syntax: SELECT column_name(s) FROM table_name LIMIT number Example: SELECT * FROM Persons LIMIT 5 Oracle…
SQL | The DELETE Statement
DELETE FROM table_name WHERE some_column=some_value Note: Notice the WHERE clause in the DELETE syntax. The WHERE clause specifies which record or records that should be deleted. If you omit the…
SQL The ORDER BY Keyword
SELECT column_name(s) FROM table_name ORDER BY column_name(s) ASC|DESC ORDER BY Example The “Persons” table: Now we want to select all the persons from the table above, however, we want to…
SQLÂ WHEREÂ Clause
The “Persons” table: Now we want to select only the persons living in the city “Sandnes” from the table above. We use the following SELECT statement using Where clause: The…
SQL Introduction
SQL is a standard language for accessing databases. How to use SQL to access and manipulate data in: MySQL, SQL Server, Access, Oracle, Sybase, DB2, and other database systems. SQL…
Gaussian Mixtures Models in Machine Learning
A Gaussian mixture model (GMM) is a probabilistic model that assumes that the instances were generated from a mixture of several Gaussian distributions whose parameters are unknown. All the instances…
Introduction to Unsupervised Learning Techniques
Introduction Although most of the applications of Machine Learning today are based on supervised learning (and as a result, this is where most of the investments go to), the vast…
Pyspark Questions and Answers
Q1: Write pyspark code to create dataframe and print with ‘color’ and ‘weight’ as separate columns. inventoryData = schema = Product
Kernel PCA in Machine Learning
The post discusses Kernel Principal Component Analysis (kPCA), highlighting its application in nonlinear dimensionality reduction and suggesting methods for selecting kernels and tuning hyperparameters through grid search and reconstruction pre-image…
Hierarchical Clustering in Machine Learning
The content discusses K-Means and Hierarchical Clustering algorithms. K-Means requires predefined clusters and is sensitive to initial centroids and outliers. Hierarchical Clustering offers an agglomerative and divisive approach without preset…
Second or Third Highest Record in SQL
When data is given to us and we want to find out Top 3 or Bottom 5 records on the basis of some attribute, this can be easily solved by…
Choosing the Right Number of Dimensions in Dimensionality Reduction
The content discusses dimensionality reduction using PCA, emphasizing the importance of preserving a significant portion of variance, typically 95%. It explains how to compute PCA, options for variance preservation, and…
Main Approaches for Dimensionality Reduction
This content discusses dimensionality reduction approaches, focusing on projection and Manifold Learning. It explains how projection simplifies high-dimensional data, exemplified by datasets like the Swiss roll. Principal Component Analysis (PCA)…
Managing Data from Relational Databases using Python
Databases vary widely, with relational databases being predominant due to their efficient data structuring and retrieval. SQL simplifies data manipulation across different DBMSs. Python's sqlalchemy facilitates database connections, allowing data…
Introduction to Dimensionality Reduction
The text discusses the curse of dimensionality in machine learning, highlighting challenges in high-dimensional spaces. It suggests reducing features to improve training efficiency and visualization, while addressing potential information loss…
Gradient Boosting in Machine Learning
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the…
Boosting( Ensemble) Trees | Machine Learning from Scratch
Introduction Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to…