Sumit Pandey

A Comprehensive Guide to UNet Implementation with TensorFlow

After writing article on Image Segmentation by UNet: Everything you need to know about Implementing in PyTorch, one of my friend asked me…

Hierarchical Clustering: Intro with Math and Python

Introduction Clustering is a fundamental task in unsupervised machine learning that involves grouping data points based on their similarities. Hierarchical clustering is a powerful clustering technique that builds nested clusters in a hierarchical manner. This tutorial will walk you through the process of implementing hierarchical clustering using Python, with a

Deep Learning

Summer School Lectures: GAN methods for Medical Imaging

This lecture explains deep generative models in medical imaging, covering GANs, VAEs, diffusion models, and real-world medical applications.

Deep Learning

Summer School Lectures: CNN

Video of comprehensive overview of CNN architectures, key concepts, applications, and advanced models like ResNet and EfficientNet.

Summer School Lectures:Basics of Deep Learning

These videos introduce deep learning concepts, covering basics of neural networks, activation functions, optimization, and CNN applications.

Data Science

K-means Clustering: Intro with Math and Python

Learn to implement K-Means clustering in Python using customer data, with explanations to understand how the algorithm works.

Data Science

Linear Algebra for Machine Learning: A Python Introduction

Linear algebra provides tools to handle and manipulate complex, high-dimensional data effectively. Key applications include data representation, model training, dimensionality reduction, and neural network operations.

Deep Learning

Machine Learning Programming Skills

Learn foundational machine learning concepts and techniques. This tutorial covers Linear Regression, Logistic Regression, Decision Trees, and K-Nearest Neighbors with practical examples and Python code.

Data Science

Building a Foundation in Data Science

This article illustrates the foundation of data science, covering Basic Data Concepts, Data Collection and Cleaning, and Data Visualization, with illustrations for each section.

Understanding AI and ML Fundamentals

Hi welcome back 😄. In the last article, I mentioned explained roadmap to learn AI and ML for people with non math background. This article aims to provide a foundational understanding of these technologies by covering their basic concepts, history, key terminologies, types, and real-world applications. Introduction to AI and ML

Deep Learning

Roadmap for AI and ML for Non Mathematical Background

Roadmap to simplify AI and Machine Learning (ML) for non-math backgrounds, exploring intuitive fundamentals and practical applications.

Deep Learning

Building my own “Comic GPT” using OpenAI’s GPT store .

Build your own

Protect Your Images Copyright From Research Publications

Protect Your Images Copyright From Research Publications

Data Science

Regression using Random forest on Custom Dataset

Supervised learning involves training a model on a labeled dataset to predict outcomes. Today, we’ll explore this using a Medical Cost dataset.

Data Science

ML model: Fine Tune Hyperparameters

Tuning hyperparameters significantly improves model performance using Grid Search and Cross-Validation

Deep Learning

Python for Deep Learning and Machine Learning.

Learn deep learning with Python from scratch! Start your journey with hands-on coding exercises and overcome your Python limitations for deep learning.