How to Learn PyTorch From Scratch in 2025: An Expert Guide

Why Learning PyTorch is So Beneficial

With the current AI bubble continuing to expand and PyTorch being the main AI framework, the demand for PyTorch specialists has never been higher. It is used to build everything from simple image classifiers to large language models like GPT, computer vision systems for self-driving cars, recommendation engines for streaming services, and cutting-edge AI research projects in labs around the world.

Also, according to the 2024 StackOverflow Developer Survey, PyTorch is the fourth most popular Python framework:

This high ranking reflects PyTorch’s importance in the data science and machine learning industry. 

Companies are actively seeking professionals with PyTorch skills, offering competitive salaries and exciting career opportunities. The framework’s growing popularity also means there’s a vast community of developers and researchers sharing knowledge, tutorials, and resources. 

Whether you’re just starting your AI journey or looking to advance your career, learning PyTorch can open doors to cutting-edge projects and collaborations with leading tech companies.

How Long Does it Take to Learn PyTorch?

While PyTorch is a beginner-friendly framework, going from a total beginner to a practitioner who can solve problems with PyTorch takes some time. The time it takes to learn PyTorch can vary greatly depending on your prior experience with programming, the complexity of the concepts you’re trying to grasp, and the amount of time you can dedicate to learning.

But with a structured learning plan and consistent effort, you can master the fundamentals in a couple of weeks and become comfortable with it in a few months.

Online resources are always welcoming and provide a better learning experience than PyTorch documentation (that is not to say the docs are poor — they are excellent). You can choose them based on your current skill level and how much time commitment you have. 

For example, our Deep Learning in Python skill track that primarily uses PyTorch takes around 16 study hours to finish and covers skills from beginner to intermediate. Of course, the journey to become a skilled deep learning engineer in Python takes much more time and effort than that. 

Much of your time should be spent writing PyTorch code and solving problems.

Just like learning to ride a bike is much more effective when you actually get on one rather than reading about cycling techniques, diving into real PyTorch projects accelerates your learning far more than theoretical study alone. At DataCamp, we offer many PyTorch projects to get your hands dirty.

How to Learn PyTorch: 6 Steps for Success

Now, let’s see a step-by-step approach to learning PyTorch. This article assumes that you are comfortable with Python and have experience working with various types of data. If not, you’ll want to first start with our How to Learn Python guide and our Python Fundamentals skill track. 

Step 1 — Understand why you’re learning PyTorch

Before starting to learn PyTorch, it is important to set clear goals. Since PyTorch is such a vast framework used in all kinds of applications, you may end up not making adequate progress by trying to learn everything at once.

Take a moment to think about what kind of projects excite you: 

• Are you more interested in natural language processing (NLP) tasks and applications like LLMs? 
• Maybe, processing and generating human-like audio is more your style (personally, I would love a way to unlock my iPhone with my voice). 
• You might also work on an amazing array of computer vision tasks involving image and audio. 

Whichever sub-task of deep learning you choose, your learning experience will be different. PyTorch uses various approaches and algorithms to solve these tasks, so choosing one beforehand will set the direction of your learning path beyond the fundamentals.

Step 2 — Get started with PyTorch basics

PyTorch emphasizes intuitive deep learning development and allows you to express complex neural networks in readable code. You’ll want to start by understanding the fundamental building blocks:

• Tensors: The core data structure in PyTorch, similar to NumPy arrays but with GPU acceleration capabilities
• Autograd: PyTorch’s automatic differentiation engine that powers neural network training
• Neural Network Modules: Building blocks for creating neural network architectures

Our Introduction to Deep Learning in PyTorch course covers many of these fundamentals, starting you on your journey toward PyTorch mastery. 

Installing PyTorch and Setting Up Your Environment

To start using PyTorch, you’ll need to install it and set up your development environment. You can install PyTorch using pip or conda, selecting the appropriate version for your system and optional CUDA support for GPU acceleration.

Step 3 — Write Your First PyTorch Program

Begin with simple tensor operations and basic neural network components. Create tensors, perform mathematical operations, and understand how data flows through the computation graph. Start with implementing linear regression or a basic classifier before moving to more complex architectures.

PyTorch data structures

Beyond tensors, PyTorch provides several specialized data structures and utilities:

• Dataset and DataLoader classes for handling data
• nn.Module for building neural network layers
• Optimizers for updating model parameters

Training flow in PyTorch

Understanding the training loop is crucial — forward pass, loss calculation, backpropagation, and parameter updates form the core of deep learning in PyTorch. These concepts are fundamental whether you’re building a simple neural network or a complex transformer model. You will likely end up memorizing the loop steps by heart.

Loss functions and optimizers

PyTorch provides various loss functions for different tasks (MSE, Cross Entropy, etc.) and optimizers (SGD, Adam) to update model parameters. Mastering these components is essential for training effective models.

Step 4 — Master intermediate PyTorch concepts

Once you’re familiar with the basics, you can start moving on to more advanced PyTorch topics. These are essential for building sophisticated deep learning models and will help you tackle various challenges in neural network development.

Autograd and backpropagation

PyTorch’s autograd system is crucial for automatic differentiation and gradient computation. At this stage, you will learn it beyond its basics, including how to create computation graphs and handle gradients. These form the backbone of effective neural network training.

Working with PyTorch’s ecosystem

PyTorch’s power comes from its rich ecosystem of tools and extensions. Beyond the core torch package, learn to use important modules like torchvision for computer vision, torchaudio for audio processing, and torchtext for natural language processing. These domain-specific libraries provide pre-built models and utilities for specialized tasks.

Object-oriented model development

PyTorch follows an object-oriented approach through nn.Module. Understanding how to create custom layers, define model architectures, and implement complex neural networks using class inheritance is crucial for building sophisticated models. This includes concepts like forward hooks, parameter management, and model serialization.

Advanced training techniques

Master advanced training concepts like learning rate scheduling, gradient clipping, and early stopping. Understanding these optimization techniques is essential for training deep networks that converge effectively and generalize well to new data. Through these techniques, you will have a better grasp on managing compute resources like GPU memory and RAM.

Step 5 — Learn by doing

As we mentioned earlier, project-based learning is absolutely essential for mastering PyTorch effectively. Projects force you to actively use the skills you have learned in courses and tutorials, imprinting the techniques in your memory far more effectively.

Thankfully, many DataCamp resources use this learn-by-doing method, but here are some other ways to practice your skills:

• Take on projects that excite you: look around and see if any problems in your or your family’s life can be solved with PyTorch.
• Attend webinars and code-alongs: You’ll find plenty of DataCamp webinars and online events where you can code along with the instructor. This method can be a great way to learn new concepts and see how they’re applied in real time.

If you are lacking project ideas, DataCamp offers a wide range of PyTorch projects you can implement yourself:

• Building an E-Commerce Clothing Classifier Model
• Developing Multi-Input Models For OCR
• Service Desk Ticket Classification with Deep Learning 
• Classifying X-Ray Images using PyTorch
• Predicting Traffic Volume with PyTorch

An Example PyTorch Learning Plan

Based on what we’ve covered, below is a sample week-by-week learning plan that you can adjust based on your time commitment.

Daily practices:

• 3 hours of focused coding
• Documentation reading and note-taking
• Code review and refactoring
• Problem-solving exercises

Weekly goals:

• Complete one significant project
• Write technical documentation
• Participate in PyTorch community discussions
• Create a portfolio entry
• Review and optimize the previous week’s code

Week 1: PyTorch Basics and Environment Setup

• Monday: Install PyTorch, set up development environment, verify GPU support
• Tuesday: Learn tensor creation, indexing, and basic operations
• Wednesday: Practice tensor manipulations and mathematical operations
• Thursday: Study autograd mechanics and computational graphs
• Friday: Explore basic neural network components
• Weekend: Build and train a simple linear regression model

Week 2: Neural Network Foundations

• Monday: Study different loss functions (MSE, Cross-Entropy)
• Tuesday: Learn about optimizers (SGD, Adam, RMSprop)
• Wednesday: Implement various activation functions
• Thursday: Build your first neural network using nn.Module
• Friday: Learn data loading and preprocessing
• Weekend: Create a digit classifier using the MNIST dataset

Week 3: Training Deep Neural Networks

• Monday: Master the training loop components
• Tuesday: Implement validation and testing procedures
• Wednesday: Learn about learning rate scheduling
• Thursday: Study batch normalization and dropout
• Friday: Implement early stopping and model checkpointing
• Weekend: Train a fashion classifier using Fashion-MNIST

Week 4: Computer Vision and CNNs

• Monday: Learn CNN architecture fundamentals
• Tuesday: Study different CNN layers and operations
• Wednesday: Implement popular CNN architectures
• Thursday: Practice transfer learning with pre-trained models
• Friday: Learn data augmentation techniques
• Weekend: https://app.datacamp.com/learn/projects/2215

Week 5: Advanced Model Development

• Monday: Study sequence models and RNNs
• Tuesday: Learn LSTM and GRU architectures
• Wednesday: Implement attention mechanisms
• Thursday: Study transformer architecture basics
• Friday: Learn model deployment strategies
• Weekend: Create a text classification model

Week 6: Production and Optimization

• Monday: Learn model quantization techniques
• Tuesday: Study model pruning and compression
• Wednesday: Practice model serving and API creation
• Thursday: Learn about distributed training
• Friday: Study GPU memory optimization
• Weekend: Deploy a model with FastAPI

Week 7: Real-world Applications

• Monday: Learn about custom datasets and data pipelines
• Tuesday: Study multi-GPU training
• Wednesday: Implement custom loss functions
• Thursday: Learn debugging and profiling techniques
• Friday: Study best practices for production
• Weekend: Build an end-to-end ML pipeline

Week 8: Advanced Topics and Specialization

• Monday: Explore generative models (GANs, VAEs)
• Tuesday: Study reinforcement learning basics
• Wednesday: Learn about graph neural networks
• Thursday: Practice with advanced optimization techniques
• Friday: Study model interpretability methods
• Weekend: Classifying X-Ray Images using PyTorch project

6 Top Tips for Learning PyTorch

Once you are ready to get started, keep these tips in mind, as they can help you maximize your progress and keep focused.

1. Find your focus

This point is worth mentioning twice. PyTorch is a versatile framework with a wide range of applications, so decide what kind of problems you want to work on from the very beginning. The main applications are:

• Natural language processing (NLP)
• Computer vision (image and video)
• Audio processing
• Recommendation systems
• Time series forecasting and anomaly detection
• Reinforcement Learning
• Scientific computing and physics simulations

Each of the above can have a large category of sub-problems like image classification, audio generation, or text generation. People may even dedicate their entire careers to solving one of these problems.

2. Practice regularly

There is no substitute for hard work — to be good at PyTorch, you have to show up consistently and get your hands dirty. Practicing doesn’t necessarily have to be about writing code — you can read other’s PyTorch code, read up on documentation, and write tutorials or guides on what you have learned. The important thing is to not let your hard-earned PyTorch knowledge slip out of your mind or to go rust.

3. Work on projects

We mention this point once again, too. Project-based learning is the best way to build real-world PyTorch knowledge. Especially, if you solve a specific problem that has an impact on your own life, the knowledge you gained during the process will stay with you for a long time.

4. Join a community

Since PyTorch is widespread, you can find many online communities and groups to join. Your PyTorch journey doesn’t have to be solitary. In fact, joining a community of learners can provide a wide range of benefits. You can share knowledge, get help when stuck, learn from others’ experiences, and even collaborate on projects. 

Communities like PyTorch Forums, Reddit’s r/pytorch, and various Discord servers dedicated to deep learning provide excellent platforms for discussion, troubleshooting, and staying updated with the latest developments. 

Additionally, participating in these communities can lead to networking opportunities and potential job prospects as you build relationships with other PyTorch practitioners.

How to Land a Job That Uses PyTorch

While the traditional path to finding any type of programming job is by getting a degree in a related field like computer science, more and more professionals are entering the field through non-traditional paths. In this section, we will cover how to land a PyTorch job without a degree. First, let’s cover some job titles that hire PyTorch specialists.

Top PyTorch career paths

PyTorch skills are highly valued in many roles and industries. Here are some common job positions:

• Machine Learning Engineer — Build and deploy ML models using PyTorch
• Deep Learning Researcher — Conduct research and develop novel architectures
• Computer Vision Engineer — Work on image/video processing applications
• NLP Engineer — Focus on language models and text processing
• AI Research Scientist — Advance state-of-the-art in artificial intelligence
• Data Scientist — Apply PyTorch for predictive modeling and analytics
• ML Infrastructure Engineer — Build systems to support ML model deployment
• AI Application Developer — Create end-user applications powered by PyTorch
• Robotics Engineer — Use PyTorch for robotic control and perception
• AI Consultant — Help companies implement PyTorch-based solutions

Many companies hiring for these roles include:

• Tech giants (Google, Meta, Microsoft, etc.)
• AI-focused startups
• Research institutions and universities
• Healthcare and biotech companies
• Financial institutions
• Automotive and manufacturing companies

Finding PyTorch jobs

So, how do you land a job in one of these companies?

1. Develop a strong portfolio

The first thing you need is a rock-solid portfolio. It should demonstrate your ability to solve real-world problems using PyTorch, showcase your understanding of deep learning concepts, and highlight your coding skills. Include diverse projects that align with your target role, whether that’s computer vision, NLP, or general machine learning. 

Make sure to document your work well on platforms like GitHub, explaining your approach, methodology, and results. If you can, share metrics or details of how your project had an impact, whether on your life, your friends, or for a business. Having a strong portfolio not only proves your technical capabilities but also shows potential employers that you can deliver practical solutions.

2. Develop an effective resume

In the modern job market, 60–70% of applications go down the drain before humans actually look at yours. This is due to the fact that Application Tracking Systems (ATS) automatically filter candidates based on experience, keyword usage, and other factors. 

So, before even reaching humans, your resume must be effective enough to go through these systems. Fortunately, you can fight AI with AI by leveraging online services that can help you optimize your resume. You can also read our separate guide on building a standout resume.

3. Generate attention and network

One of the proven methods of landing jobs, not just in deep learning but in any field, is through networking. But many beginners get uneasy because they don’t know anyone in the field to network with. That’s where platforms like LinkedIn can be a huge help.

From day 1, you should commit to sharing your PyTorch knowledge in the form of short posts or articles on LinkedIn or Medium. This puts your name and expertise in front of potential hiring managers and can leave a lasting impression. You can connect with anyone who engages with your content, slowly building and expanding your network.

You might land freelance projects on PyTorch through your network, which can be a huge plus to your portfolio.

Final Thoughts

Learning PyTorch is a valuable investment in your career, especially as AI continues to reshape industries worldwide. While the learning curve might seem steep at first, following a structured approach and staying consistent with your practice will help you master this powerful framework. Remember that everyone starts somewhere, and the PyTorch community is incredibly supportive of newcomers.

Whether you’re aiming to become a machine learning engineer, a researcher, or simply want to explore the fascinating world of deep learning, PyTorch provides the tools and flexibility you need. Start with the basics, work on meaningful projects, engage with the community, be consistent, and don’t rush. There’s never been a better time to start your PyTorch journey.

Get started today with our Introduction to Deep Learning in PyTorch Course, or continue your journey with Intermediate Deep Learning with PyTorch Course. You can also discover what it takes to get a PyTorch certification in our separate guide.