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

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.

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.

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.

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.

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.

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

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.

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

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.

ML model: Fine Tune Hyperparameters

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

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.

‘RuntimeError: CUDA out of memory’, problem solved using ‘Gradient Accumulation’

Last year, I was working on BRATs-23 segmentation challenge when I realized I can’t increase the batch size more than ‘one’ because…

Deep Learning

Pytorch Tutorial Part 2: Model, Training and testing

In this article, we'll learn about building, training, and testing a PyTorch model. Building the model was the hardest part for me. Let's start now with defining, initializing, and connecting layers in the model, followed by training and testing it.

Deep Learning

Pytorch Tutorial Part 1: Dataset and Dataloader

Explore image classification and segmentation with PyTorch, covering dataset setup and data loaders in this beginner-friendly guide.

Deep Learning

Intro to CNN (Convolutional Neural Network)

Learn the basics of Convolutional Neural Networks (CNNs) and how they process images through layers, from input to output.

Deep Learning

Practical Introduction to Deep Learning with PyTorch

A few weeks ago, I was having a chat with my friend when he asked me to teach him Deep Learning in the simplest way possible. The first…

Image Segmentation by UNet: Everything you need to know about Implementing in PyTorch

Image segmentation is a process in computer vision where an image is divided into different parts, with each part representing a specific…

Deep Learning

Explainable YOLOv8 with GRADCAM

Loving YOLOv8 ? It’s great but lacks built-in Explainable AI features like GRAD-CAM. Found a GitHub repo to integrate EigenCAM for better transparency.

Deep Learning

🤗 Huggingface Spaces: Ideal place to show your skills as newbie Data Scientist/ML Engineer

Showcase you AI skills by hosting your models at Huggingface for free

Deep Learning

MultiPlanar UNet: For 3-D Segmentation Tasks - Low Code

Struggling with limited data for 3D medical image segmentation? Try MultiPlanar UNet! Achieved >0.80 dice score on a small knee MRI dataset.

Deep Learning

DINOv2 for Custom Dataset Segmentation: A Comprehensive Tutorial.

Discover DINOv2, a powerful self-supervised vision transformer trained on 142M images. This tutorial guides you through data loading, preprocessing, model definition, and training using PyTorch.

Machine Learning

Practical Machine Learning Problems

Machine learning faces real-world challenges like data quality, feature selection, and model deployment. This article explores strategies to overcome these issues.

Understanding Deep Learning in a Comic way.

Do you know how Deep Learning works ? By the way deep learning is the core technology of almost every AI model nowadays you see around…

Machine Learning

Classification using Random Forest

Learn supervised learning with Random Forest using the iris dataset. This tutorial covers data preprocessing, model training, and results visualization using Python.

Deep Learning

Combining YOLOv8 and SAM for Image Segmentation

Combining YOLO and SAM for better segmentation results

Deep Learning

YOLOv8 Segmentation on Custom Dataset (Tutorial)

YOLOv8 is an amazing segmentation model, its easy to train test and deploy. In this tutorial we will learn how to use YOLOv8 on custom dataset

Data Science

Identifying Outliers in Data: A Comprehensive Guide

Learn about Z-score and IQR methods for detecting outliers in data analysis. Understand their workings, strengths, and weaknesses.