Programming, AI, Machine Learning & Software Development Tutorials
Introduction: Dynamic Programming (DP) is a powerful algorithmic technique used to solve a variety of optimization problems. One classic problem where DP shines is in finding the longest palindromic subsequence in a given string. In this tutorial, we’ll delve into the intricacies of this problem and learn how to tackle it using DP. Problem Statement: … Read more
You are given a string ‘S’. Your task is to check whether the string is palindrome or not. For checking palindrome, consider alphabets and numbers only and ignore the symbols and whitespaces. Note : Example : Input format : Output format : Note : Follow Up : Constraints : Implementation in Python
For Linear Regression in Machine learning with two variables we have to find 2 coefficient. In case of overfitting these 2 coefficients can be very high. Y=mX+c So to handle this, Idea is that we want to reduce the value of coefficient(m). By doing this biasness can increase but variance decreases. Which is called bias … Read more
Introduction I decided to write this kernel because Titanic: Machine Learning from Disaster is one of my favorite competitions on Kaggle. This is a beginner level kernel which focuses on Exploratory Data Analysis and Feature Engineering. A lot of people start Kaggle with this competition and they get lost in extremely long tutorial kernels. This is a short kernel compared … Read more
Introduction Feature engineering, often hailed as the cornerstone of machine learning, holds the power to transform raw data into actionable insights. In the realm of predictive modeling, where the quality of features can significantly influence model performance, mastering the art of feature engineering is indispensable. In this comprehensive guide, we’ll embark on a Titanic journey … Read more
With thousands of parameters you can fit the whole zoo. Deep neural networks typically have tens of thousands of parameters, sometimes even millions. With so many parameters, the network has an incredible amount of freedom and can fit a huge variety of complex datasets. But this great flexibility also means that it is prone to … Read more
Introduction In the dynamic landscape of machine learning, Multilayer Perceptrons (MLPs) emerge as formidable tools capable of handling both regression and classification tasks with finesse. Whether you’re predicting housing prices or sorting emails, understanding how to tailor MLP architectures and activations is pivotal for optimizing performance. Regression MLPs Crafting an MLP architecture for regression tasks … Read more
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). The layers close to the input layer are usually called the lower layers, and the ones close to the outputs are usually called the upper … Read more
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 the prisoners around a circular table in sequentially numbered chairs. A chair number will be drawn from a hat. Beginning with the prisoner in that … Read more
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 use a depth-first search (DFS) approach, exploring the numbers in lexicographical order. Let’s dive into the implementation. Also checkout: Implementation: Explanation: Example: For n = … Read more
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 Syntax SELECT column_name(s) FROM table_name WHERE ROWNUM <= number Example SELECT * FROM Persons WHERE ROWNUM <=5 SQL TOP Example The “Persons” table: Now we … Read more
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 WHERE clause, all records will be deleted! SQL DELETE Example The “Persons” table: Now we want to delete the person “Tjessem, Jakob” in the “Persons” … Read more
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 sort the persons by their last name.We use the following SELECT statement: The result-set will look like this: ORDER BY DESC Example Now we want … Read more
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 result-set will look like this: Quotes Around Text Fields in SQL WHERE CLAUSE SQL uses single quotes around text values (most database systems will also … Read more
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 Syntax: SELECT Company, Country FROM Customers WHERE Country <> ‘USA’ SQL Result: SQL is a “standard language for accessing and manipulating databases”. What Can SQL … Read more
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 generated from a single Gaussian distribution form a cluster that typically looks like an ellipsoid. Each cluster can have a different ellipsoidal shape, size, density … Read more
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 majority of the available data is actually unlabeled: we have the input features X, but we do not have the labels y. Yann LeCun famously … Read more
Q1: Write pyspark code to create dataframe and print with ‘color’ and ‘weight’ as separate columns. inventoryData = [ (‘Laptop’, 20, {‘color’: ‘silver’, ‘weight’: 2.5}), (‘Phone’, 50, {‘color’: ‘black’, ‘weight’: 0.5}), (‘Tablet’, 30, {‘color’: ‘white’, ‘weight’: 0.8}), (‘Chair’, 10, {‘color’: ‘brown’, ‘weight’: 3.0}), (‘Printer’, 15, {‘color’: ‘gray’, ‘weight’: 7.5}), ] schema =[“Product”, “Quantity”, “Properties”] Product … Read more
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 error minimization.
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 clusters. The document also explores various linkage methods, dendrograms for visualization, and the validity of clusters over time.