Practical overview
When it fits
Use it as a starting point for learning deep-learning concepts and PyTorch, especially if you are new to the subject.
Who it is for
- Readers new to deep learning.
- Learners without a strong mathematics background who want a mathematically light introduction.
Documented uses
- Build an initial understanding of artificial neural networks.
- Get an introduction to PyTorch.
- Begin exploring how computers interpret visual information.
Requirements & limits
The book is described as open source and is published in a public GitHub repository under the GPL-3.0 license.
- The project describes itself as a first draft, and its chapter on how computers remember is marked unfinished.
- The repository invites contributions including improved explanations, beginner PyTorch examples and Google Colab notebooks.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Study the introductory deep-learning book
Use the book’s listed chapters to begin learning neural networks and PyTorch.
Before you start
- No prior deep-learning experience or strong mathematics background is required, according to the README.
Steps
Open the repository README.
Start with Chapter 1, “Understanding Artificial Neural Networks.”
Continue to Chapter 2, “Introduction to pyTorch,” then Chapter 3, “How to make a computer see?”
Use Chapter 5, “Where to go from here?” as the listed next step.
Expected outcome
A beginner can follow the book’s listed sequence through neural networks, PyTorch, and computer vision.
Guide limitations
- The README identifies the book as a first draft that is still being written.
- Chapter 4, “How to make a computer remember stuff?”, is marked incomplete.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- vaibhawvipul/First-steps-towards-Deep-Learning · Official homepage title · retained in its source language