Beyond Labels: The Magic of Autoencoders in Unsupervised Learning
In a world where labeled data is often scarce, autoencoders provide a powerful solution for extracting insights from unstructured data
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shivamshinde92722
Oct 92 min read
Efficient Backpropagation: Exploring the Role of Jacobian Matrices and the Chain Rule
In this article, I will tough upon the topic of Jacobian matrix and how it is used in the back-propagation operation of deep learning.
shivamshinde92722
Sep 122 min read
The Tug of War: Accuracy and Interpretability in Machine Learning Models
Model prediction accuracy is crucial, but not always the top priority. Sometimes, we may need to sacrifice some accuracy to make the model m
shivamshinde92722
Feb 95 min read
Unlocking the Potential of Pandas: Must-Know Methods for Efficient Data Handling in Python
In this article, you will learn the very important and must-know methods present in the Python Pandas library.
shivamshinde92722
Jan 276 min read
From Good to Great: Elevating Model Performance through Hyperparameter Tuning
This article will explain the concept of hyperparameter tuning and the different methods that are used to perform this tuning, and their imp
shivamshinde92722
Jan 237 min read
Guardrails for ML and DL Models: A Deep Dive into Regularization Techniques
This article will explain the concept of regularization in ML and DL. The article will also introduce readers to some of the most famous reg
shivamshinde92722
Jan 207 min read
Pause for Performance: The Guide to Using Early Stopping in ML and DL Model Training
This article will explain the concept of early stopping, its pros and cons, and its implementation using Scikit-Learn and TensorFlow.
shivamshinde92722
Jan 127 min read
Data Reliability 101: A Practical Guide to Data Validation Using Pydantic in Data Science Projects
This article will explain Why data validation is needed for the Python code, How it’s done using the Pydantic library, and How to integrate
shivamshinde92722
Jan 74 min read
A Step-by-Step Guide to Building an End-to-End Machine Learning Project
This article will show you the way you can create an end-to-end machine learning project.
shivamshinde92722
Jan 65 min read
From Raw to Refined: A Journey Through Data Preprocessing — Part 6: Imbalanced Datasets
This article will explain the concept of imbalanced datasets and the methods used to handle them.
shivamshinde92722
Aug 31, 20236 min read
From Raw to Refined: A Journey Through Data Preprocessing — Part 5: Outliers
A Simple Guide to Navigating Data Anomalies. Decode the mystery behind outliers in data science. From detection to resolution, empower your
shivamshinde92722
Aug 30, 20235 min read
From Raw to Refined: A Journey Through Data Preprocessing — Part 4: Data Encoding
Humans can understand textual information. However, this is not the case for machines and any algorithms that machines run. Machines and alg
shivamshinde92722
Aug 27, 20234 min read
From Raw to Refined: A Journey Through Data Preprocessing — Part 3: Duplicate Data
This article will explain how to identify duplicate records in the data and, the different ways to deal with the problem of having duplicate
shivamshinde92722
Aug 5, 20236 min read
From Raw to Refined: A Journey Through Data Preprocessing - Part 2: Missing Values
This article will explain the concept of missing values in the data and the ways to deal with data containing missing values.
shivamshinde92722
Jul 29, 20233 min read
From Raw to Refined: A Journey Through Data Preprocessing — Part 1: Feature Scaling
This article is the part 1 of the Data Preprocessing series. In this part, I explain the feature scaling step of the preprocessing.
shivamshinde92722
Jul 22, 20234 min read
Be Confident in your Machine Learning Models with the help of Cross-Validation
Cross-validation is a go-to tool to check if your machine-learning model is reliable enough to work on new data. This article will discuss c
shivamshinde92722
Jul 15, 20236 min read
From Chaos to Order: Harnessing Data Clustering for Enhanced Decision-Making
This article will show the important use cases of data clustering methods, how to use these methods, and also show how one can use these met
shivamshinde92722
Jul 2, 20234 min read
From Many to Few: Tackling High-Dimensional Data with Dimensionality Reduction in Machine Learning
This article will discuss the curse of dimensionality in machine learning problems and dimensionality reduction as a solution for the...
shivamshinde92722
Jun 29, 20233 min read
Unlocking the Potential of Text: A Closer Look at Pre-Embedding Text Cleaning Methods
This article will discuss different cleaning techniques that are essential to obtain maximum performance from textual data
shivamshinde92722
Apr 14, 20234 min read
Striking the Right Balance: Understanding Underfitting and Overfitting in Machine Learning Models
This article will explain the basic concept of overfitting and underfitting from the machine learning and deep learning perspective.
shivamshinde92722
Jan 27, 20235 min read
Transform Your Data Science Project: Discover the Benefits of Storing Variables in a YAML File
This blog post will discuss the benefits of using a YAML file as a central repository for storing variables, parameters, and hyper-parameter
shivamshinde92722
Jan 14, 20234 min read
Deep Dive into Gated Recurrent Units (GRU): Understanding the Math behind RNNs
Gated Recurrent Unit (GRU) is a simplified version of Long Short-Term Memory (LSTM). Let’s see how it works in this article.
shivamshinde92722
Jan 6, 20233 min read
From Vanilla RNNs to LSTMs: A Practical Guide to Long Short-Term Memory
Long short-term memory (LSTM) networks have become a go-to tool for tasks like machine translation, language modeling, and speech recognitio
shivamshinde92722
Dec 23, 20223 min read
A Simple Approach to Creating Custom Transformers Using Scikit-Learn Classes
In this article, I will be explaining how to create a transformer according to our processing needs using Scikit-Learn classes
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