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Deep Learning With Pytorch Tutorials

A public GitHub repository of companion source code and presentation materials for an introductory, practical deep-learning and PyTorch video course, with README setup instructions for Anaconda, CUDA 10.0 and related Python libraries.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Deep Learning With Pytorch Tutorials — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it alongside the linked course or to explore its provided examples and presentation materials; consult the README when setting up the specifically listed environment.

Who it is for

  • Learners following an introductory deep-learning and PyTorch course who want companion examples and slides.
  • Instructors looking for example code and presentation materials for lessons on neural-network topics.

Documented uses

  • Study PyTorch examples alongside course instruction.
  • Review presentation materials and the course outline.
  • Set up the Python dependencies listed in the repository README.
Before you choose

Requirements & limits

The repository is publicly visible on GitHub. The README links to a course page; no course pricing or account requirements are specified here.

  • The repository is course companion material rather than a hosted learning platform, model, API or development toolkit.
  • The documented setup specifies CUDA 10.0, so adapting it to newer software requires modification.
  • The README links to the course and its outline, alongside the repository materials.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the documented PyTorch tutorial environment

Set up the Anaconda, CUDA 10.0, PyTorch and library dependencies listed in the repository README.

Before you start

  • Anaconda
  • CUDA 10.0

Steps

  1. Configure the Anaconda channels listed in the README, including its PyTorch channel.

  2. Install PyTorch, torchvision and cudatoolkit=10.0 with the documented conda command.

  3. Install NumPy, Matplotlib, Pillow and pandas with the documented pip command.

Expected outcome

The listed Python packages and PyTorch dependencies are installed according to the repository’s instructions.

Guide limitations

  • The instructions specify CUDA 10.0 and do not demonstrate compatibility with newer software versions.
  • The commands document setup only; they do not establish that course examples run successfully in a particular environment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
18 October 2026
Original website title
dragen1860/Deep-Learning-with-PyTorch-Tutorials · Official homepage title · retained in its source language
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