🌟 Vision Transformer (ViT) Tutorial – Part 2: Implementing ViT from Scratch in PyTorch

Let's start: https://hackmd.io/@husseinsheikho/vit-2

#VisionTransformer #ViTFromScratch #PyTorch #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #CodingTutorial #AttentionIsAllYouNeed

✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔥 Trending Repository: LMCache

📝 Description: Supercharge Your LLM with the Fastest KV Cache Layer

🔗 Repository URL: https://github.com/LMCache/LMCache

🌐 Website: https://lmcache.ai/

📖 Readme: https://github.com/LMCache/LMCache#readme

📊 Statistics:
🌟 Stars: 4.3K stars
👀 Watchers: 24
🍴 Forks: 485 forks

💻 Programming Languages: Python - Cuda - Shell

🏷️ Related Topics:

#fast #amd #cuda #inference #pytorch #speed #rocm #kv_cache #llm #vllm

==================================
🧠 By: https://t.iss.one/DataScienceM

🔥 Trending Repository: supervision

📝 Description: We write your reusable computer vision tools. 💜

🔗 Repository URL: https://github.com/roboflow/supervision

🌐 Website: https://supervision.roboflow.com

📖 Readme: https://github.com/roboflow/supervision#readme

📊 Statistics:
🌟 Stars: 34K stars
👀 Watchers: 211
🍴 Forks: 2.7K forks

💻 Programming Languages: Python

🏷️ Related Topics:

#python #tracking #machine_learning #computer_vision #deep_learning #metrics #tensorflow #image_processing #pytorch #video_processing #yolo #classification #coco #object_detection #hacktoberfest #pascal_voc #low_code #instance_segmentation #oriented_bounding_box

==================================
🧠 By: https://t.iss.one/DataScienceM

🔥 Trending Repository: vllm

📝 Description: A high-throughput and memory-efficient inference and serving engine for LLMs

🔗 Repository URL: https://github.com/vllm-project/vllm

🌐 Website: https://docs.vllm.ai

📖 Readme: https://github.com/vllm-project/vllm#readme

📊 Statistics:
🌟 Stars: 55.5K stars
👀 Watchers: 428
🍴 Forks: 9.4K forks

💻 Programming Languages: Python - Cuda - C++ - Shell - C - CMake

🏷️ Related Topics:

#amd #cuda #inference #pytorch #transformer #llama #gpt #rocm #model_serving #tpu #hpu #mlops #xpu #llm #inferentia #llmops #llm_serving #qwen #deepseek #trainium

==================================
🧠 By: https://t.iss.one/DataScienceM

🔥 Trending Repository: LLMs-from-scratch

📝 Description: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

🔗 Repository URL: https://github.com/rasbt/LLMs-from-scratch

🌐 Website: https://amzn.to/4fqvn0D

📖 Readme: https://github.com/rasbt/LLMs-from-scratch#readme

📊 Statistics:
🌟 Stars: 64.4K stars
👀 Watchers: 589
🍴 Forks: 9K forks

💻 Programming Languages: Jupyter Notebook - Python

🏷️ Related Topics:

#python #machine_learning #ai #deep_learning #pytorch #artificial_intelligence #transformer #gpt #language_model #from_scratch #large_language_models #llm #chatgpt

==================================
🧠 By: https://t.iss.one/DataScienceM

🔥 Trending Repository: LLMs-from-scratch

📝 Description: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

🔗 Repository URL: https://github.com/rasbt/LLMs-from-scratch

🌐 Website: https://amzn.to/4fqvn0D

📖 Readme: https://github.com/rasbt/LLMs-from-scratch#readme

📊 Statistics:
🌟 Stars: 68.3K stars
👀 Watchers: 613
🍴 Forks: 9.6K forks

💻 Programming Languages: Jupyter Notebook - Python

🏷️ Related Topics:

#python #machine_learning #ai #deep_learning #pytorch #artificial_intelligence #transformer #gpt #language_model #from_scratch #large_language_models #llm #chatgpt

==================================
🧠 By: https://t.iss.one/DataScienceM

📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read

Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.

#PyTorch #MachineLearning #NeuralNetwork #Regression #Python

📌 Learning Triton One Kernel at a Time: Softmax

🗂 Category: MACHINE LEARNING

🕒 Date: 2025-11-23 | ⏱️ Read time: 10 min read

Explore a step-by-step guide to implementing a fast, readable, and PyTorch-ready softmax kernel with Triton. This tutorial breaks down how to write efficient GPU code for a crucial machine learning function, offering developers practical insights into high-performance computing and AI model optimization.

#Triton #GPUProgramming #PyTorch #MachineLearning

📌 Overcoming the Hidden Performance Traps of Variable-Shaped Tensors: Efficient Data Sampling in PyTorch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-12-03 | ⏱️ Read time: 10 min read

Unlock peak PyTorch performance by addressing the hidden bottlenecks caused by variable-shaped tensors. This deep dive focuses on the critical data sampling phase, offering practical optimization strategies to handle tensors of varying sizes efficiently. Learn how to analyze and improve your data loading pipeline for faster model training and overall performance gains.

#PyTorch #PerformanceOptimization #DeepLearning #MLOps

📌 YOLOv1 Paper Walkthrough: The Day YOLO First Saw the World

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-12-05 | ⏱️ Read time: 17 min read

A deep dive into the original YOLOv1 paper, exploring the revolutionary "You Only Look Once" algorithm. This technical walkthrough breaks down the foundational object detection architecture and guides readers through a complete implementation from scratch using PyTorch. It's an essential resource for understanding the core mechanics of single-shot detectors and the history of computer vision.

#YOLO #ObjectDetection #ComputerVision #PyTorch

📌 On the Challenge of Converting TensorFlow Models to PyTorch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-12-05 | ⏱️ Read time: 19 min read

Converting legacy TensorFlow models to PyTorch presents significant challenges but offers opportunities for modernization and optimization. This guide explores the common hurdles in the migration process, from architectural differences to API incompatibilities, and provides practical strategies for successfully upgrading your AI/ML pipelines. Learn how to not only convert but also enhance your models for better performance and maintainability in the PyTorch ecosystem.

#PyTorch #TensorFlow #ModelConversion #MLOps #DeepLearning