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.
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