Pytorch

Introduction TL;DR: PyTorch, developed by Meta, is a prominent deep learning framework utilizing a Define-by-Run (Dynamic Computation Graph) approach, which significantly aids intuitive model development and debugging. Its core strength lies in GPU acceleration via Tensor objects and automatic differentiation through Autograd. With the latest stable version being PyTorch 2.9.0 (as of October 2025), PyTorch continues to evolve its ecosystem, offering robust tools like TorchScript and ONNX for production deployment, making it a powerful, Python-centric platform for both research and industry applications. PyTorch is an open-source machine learning library designed to accelerate the path from research prototyping to production deployment. This article explores the core architectural features that make PyTorch a preferred choice for many developers and outlines its practical application in real-world environments. Core Architecture and Flexibility 1. Tensors and GPU Acceleration In PyTorch, a Tensor is the fundamental data structure, analogous to NumPy arrays but with crucial support for GPU (Graphics Processing Unit) acceleration. This capability is essential for handling the massive computational loads of modern deep learning models. By simply moving a Tensor to a CUDA device, complex matrix operations are parallelized, drastically reducing model training time. ...

AI Development Environment Setup: Anaconda, Jupyter, and GPU Acceleration (Lecture 4) In this lecture, we’ll set up a stable AI development environment for Machine Learning and Deep Learning projects. You’ll learn how to install Anaconda, run Jupyter Notebook, and configure GPU acceleration with CUDA and cuDNN. Table of Contents {% toc %} 1) Why Environment Setup Matters A well-configured environment prevents common issues such as: Library version conflicts Slow training due to CPU-only execution Non-reproducible results across team members Goals: ...

Mastering CartPole with DQN: Deep Reinforcement Learning for Beginners If you’ve played with reinforcement learning (RL) before, you’ve probably seen the classic CartPole balancing problem. And if you’ve tried solving it with traditional Q-learning, you might have run into some limitations. That’s where DQN — Deep Q-Network — comes in. In this guide, we’ll explain what DQN is, why it was a breakthrough in RL, and how to implement it step-by-step to solve the CartPole-v1 environment using OpenAI Gym and PyTorch. Whether you’re new to RL or ready to level up from Q-tables, this tutorial is for you. ...