DigitalSreeni

Welcome to my Python coding channel! Here, I'll teach you everything from the very basics to advanced topics in machine learning and deep learning. I'll focus a lot on image processing and other relevant topics.

How to cite my work?
YouTube video:
The general format for citing a YouTube video in APA (American Psychological Association) style is:
Author’s Last Name, First Initial. (Year, Month Day Published). Title of video [Video]. YouTube. URL

So, here is an example:
Bhattiprolu, S. (2023, August 23). 330 - Fine tuning Detectron2 for instance segmentation using custom data [Video]. YouTube. https://youtu.be/cEgF0YknpZw

GitHub code:
Author’s Last Name, First Initial. (Year). Title of Repository. GitHub. URL
Example:
Bhattiprolu, S. (2023). python_for_microscopists. GitHub. https://github.com/bnsreenu/python_for_microscopists/blob/master/330_Detectron2_Instance_3D_EM_Platelet.ipynb

372 — Всё о Base64

372 — Всё о Base64
371 - Advanced Dimensionality Reduction: t-SNE vs UMAP vs PCA Deep Dive

371 - Advanced Dimensionality Reduction: t-SNE vs UMAP vs PCA Deep Dive
Пять главных достижений в анализе микроскопических изображений (377)

Пять главных достижений в анализе микроскопических изображений (377)
370 - Principal Component Analysis (PCA): Mastering Dimensionality Reduction & Visualization

370 - Principal Component Analysis (PCA): Mastering Dimensionality Reduction & Visualization
Развенчание мифов о классах в Python

Развенчание мифов о классах в Python
148b — Обработка несбалансированных данных в Python: подход, ориентированный на бизнес

148b — Обработка несбалансированных данных в Python: подход, ориентированный на бизнес
368 - Correlation vs Causation in Python: Understanding the Critical Difference (Part 4/4)

368 - Correlation vs Causation in Python: Understanding the Critical Difference (Part 4/4)
366b — Проверка спорных заявлений о здоровье: пошаговый анализ данных с использованием Python

366b — Проверка спорных заявлений о здоровье: пошаговый анализ данных с использованием Python
367 - Advanced Correlation Analysis in Python: Confidence Intervals & Statistical Testing (Part 3/4)

367 - Advanced Correlation Analysis in Python: Confidence Intervals & Statistical Testing (Part 3/4)
366 - Partial Correlation in Python: Controlling for Confounding Variables (Part 2/4)

366 - Partial Correlation in Python: Controlling for Confounding Variables (Part 2/4)
365 - Correlation Analysis in Python: Pearson vs Spearman Correlation

365 - Correlation Analysis in Python: Pearson vs Spearman Correlation
364 Comparing Multiple Groups with ANOVA

364 Comparing Multiple Groups with ANOVA
363b - Time Series Statistical Comparison: When Data Points Aren't Independent

363b - Time Series Statistical Comparison: When Data Points Aren't Independent
Understanding AWS S3 Vectors - for AI + Science

Understanding AWS S3 Vectors - for AI + Science
363 Comparing Two Groups (Non Parametric)

363 Comparing Two Groups (Non Parametric)
362 - Comparing Two Groups (Statistical Analysis in Python: Tutorial 4)

362 - Comparing Two Groups (Statistical Analysis in Python: Tutorial 4)
361 - Understanding Data Distributions (Statistical Analysis in Python: Tutorial 3)

361 - Understanding Data Distributions (Statistical Analysis in Python: Tutorial 3)
360: Descriptive Statistics and Data Visualization Descriptive Statistics

360: Descriptive Statistics and Data Visualization Descriptive Statistics
359: Introduction to Statistical Analysis in Python

359: Introduction to Statistical Analysis in Python
358 Building Knowledge Graphs - LLM Enhanced Approach

358 Building Knowledge Graphs - LLM Enhanced Approach
357 Building an AI Powered Video Recommender - Knowledge Graphs, NLP, NetworkX Tutorial

357 Building an AI Powered Video Recommender - Knowledge Graphs, NLP, NetworkX Tutorial
356 Building a Learning Path Recommender - Manual Construction of Knowledge Graphs in Python

356 Building a Learning Path Recommender - Manual Construction of Knowledge Graphs in Python
355 Defining Knowledge Graphs with NetworkX and RDF in Python

355 Defining Knowledge Graphs with NetworkX and RDF in Python
354 - Knowledge Graphs in Python Using NetworkX library

354 - Knowledge Graphs in Python Using NetworkX library
353 - An introduction to knowledge graphs

353 - An introduction to knowledge graphs
352 - Automated Analysis of Organoid Screening Data

352 - Automated Analysis of Organoid Screening Data
351 — Простой поиск изображений с помощью графического интерфейса. Использует ViT и FAISS.

351 — Простой поиск изображений с помощью графического интерфейса. Использует ViT и FAISS.
350 - Efficient Image Retrieval with Vision Transformer (ViT) and FAISS

350 - Efficient Image Retrieval with Vision Transformer (ViT) and FAISS
349 - Understanding FAISS for efficient similarity search of dense vectors

349 - Understanding FAISS for efficient similarity search of dense vectors
348 — Поиск схожести изображений с помощью VGG16 и косинусного расстояния

348 — Поиск схожести изображений с помощью VGG16 и косинусного расстояния