<h2>About this course</h2>
<p><a href="/topics/pytorch">PyTorch</a> Tutorials is the official learning path from PyTorch, the framework powering <a href="/topics/ai-research">AI research</a> and production systems worldwide. Whether you're starting with a 60-minute blitz or diving into distributed training and <a href="/topics/reinforcement-learning">reinforcement learning</a>, these tutorials guide you through real-world applications of <a href="/topics/deep-learning">deep learning</a>. Created by the team behind PyTorch itself, they reflect industry best practices and the latest framework capabilities.</p>

<h2>What you'll learn</h2>
<ul>
  <li>Core PyTorch fundamentals: tensors, autograd, and building <a href="/topics/neural-networks">neural networks</a> from scratch</li>
  <li>Training pipelines: how to load data, define loss functions, optimize models, and validate results</li>
  <li>Convolutional neural networks (CNNs) for <a href="/topics/computer-vision">computer vision</a> tasks like image classification</li>
  <li>Recurrent neural networks (RNNs) and <a href="/topics/transformers">transformers</a> for natural language processing and sequence modeling</li>
  <li>Advanced topics: distributed training across multiple GPUs or machines, and reinforcement learning for decision-making systems</li>
  <li>Deployment patterns: exporting models and integrating them into production applications</li>
  <li>Practical debugging and profiling techniques to optimize your models</li>
</ul>

<h2>Who this is for</h2>
<p>You're ready for this course if you know Python basics and want to build AI systems beyond tutorials. You'll work with real datasets, write production-ready code, and understand why deep learning decisions matter.</p>
<ul>
  <li><strong>AI engineers and <a href="/for/researchers">researchers</a></strong> — gain hands-on mastery of the framework used in cutting-edge AI labs and papers</li>
  <li><strong>Software <a href="/for/developers">developers</a> upskilling in AI</strong> — learn to integrate deep learning models into applications and deploy them at scale</li>
</ul>

<h2>Prerequisites</h2>
<p>Solid Python programming knowledge (loops, functions, classes, libraries like NumPy). Familiarity with linear algebra and calculus concepts helps but isn't mandatory — the tutorials explain math where it matters.</p>

<h2>Why this matters for Indian learners</h2>
<p>PyTorch skills are in high demand across India's growing AI ecosystem — from startups like Postman and Flipkart's AI teams to research labs at IITs and established tech companies. Roles like ML engineer, AI researcher, and deep learning specialist command competitive salaries (₹8–20+ lakh annually for mid-level roles), and PyTorch expertise is a proven differentiator in job interviews. As Indian companies scale AI-driven products in fintech, healthcare, and e-commerce, this hands-on proficiency opens direct paths to well-paid, meaningful work.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely. PyTorch publishes these tutorials at no cost to learners worldwide — no hidden fees, no paid tier.</p>
<h3>How long will it take to complete?</h3>
<p>The course is structured for 15 hours of focused work. That's roughly 3–4 weeks at a moderate pace (1–2 hours per week), or faster if you're working intensively. The modular design lets you pick topics as needed.</p>
<h3>Will I get a certificate?</h3>
<p>No formal certificate is awarded upon completion. But the skills you build are portfolio-worthy — consider creating a GitHub project using PyTorch to showcase your learning to employers.</p>

PyTorch

Official PyTorch tutorials — from 60-minute blitz to advanced topics like distributed training and reinforcement learning.

PyTorch Tutorials

<h2>About this course</h2>
<p>This is <a href="/topics/pytorch">PyTorch</a>'s official 60-minute blitz — the fastest way to go from zero to hands-on with <a href="/topics/deep-learning">deep learning</a> fundamentals. You'll work directly with tensors, automatic differentiation, and building real <a href="/topics/neural-networks">neural networks</a>, all in one focused sprint. PyTorch is trusted by <a href="/for/researchers">researchers</a> and practitioners worldwide, and this course gives you the same mental models they use.</p>

<h2>What you'll learn</h2>
<ul>
<li>How to create and manipulate tensors — PyTorch's core data structure — and perform operations that mimic numpy but run on GPUs</li>
<li>How autograd (automatic differentiation) computes gradients for you, eliminating manual calculus in training loops</li>
<li>How to build a neural network from scratch, layer by layer, using PyTorch's <strong>nn.Module</strong> API</li>
<li>How to train a neural network using backpropagation, loss functions, and an optimizer like SGD or Adam</li>
<li>How to work with image data and build a simple convolutional neural network (CNN) for classification tasks</li>
<li>The conceptual difference between eager and graph execution, and why PyTorch's design matters for research and production</li>
<li>Practical debugging and troubleshooting patterns you'll use in every real project</li>
</ul>

<h2>Who this is for</h2>
<p>You're the ideal student if you know Python basics and want a swift, practical introduction to deep learning without weeks of theory. You don't need a PhD in math — just curiosity and a willingness to code along.</p>
<ul>
<li><strong>Engineering <a href="/for/students">students</a></strong> — gain hands-on portfolio projects and real-world skills before entering campus placements or internships</li>
<li><strong>Career-switchers and self-taught <a href="/for/developers">developers</a></strong> — move from "I've heard of neural networks" to "I can train one" in a single sitting</li>
</ul>

<h2>Prerequisites</h2>
<p>None — this course is beginner-friendly. You should be comfortable writing basic Python (variables, loops, functions) and have Python 3.x and pip installed on your machine. No linear algebra or calculus knowledge required; the course teaches what you need as you go.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's AI and data science job market is growing fast. Companies like Flipkart, Amazon, and startups across Bangalore, Hyderabad, and Delhi are hiring engineers who can move beyond theory and ship machine learning models. PyTorch proficiency is a direct skill that shows up in job postings for ML engineer, AI researcher, and data scientist roles — often at salaries 40–60% above general software engineering roles.</p>
<p>This course is also a stepping stone to advanced work in <a href="/topics/computer-vision">computer vision</a>, <a href="/topics/nlp">NLP</a>, and <a href="/topics/reinforcement-learning">reinforcement learning</a> — all high-demand areas in India's emerging AI ecosystem. One hour invested here can unlock dozens of specialized courses and projects.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. No hidden fees, no paid certificate upsell. PyTorch publishes this to help people learn.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed for 1 hour of focused work. We'd suggest blocking out 2–3 hours one weekend afternoon or evening so you can pause, experiment, and let ideas settle. Don't rush it — typing out the code yourself (not just reading) makes a huge difference.</p>
<h3>Will I get a certificate?</h3>
<p>No, this course doesn't offer a certificate. It's designed for learning, not credentialing. Once you finish, the real proof is the code you can write and projects you can build.</p>

PyTorch's official 60-minute blitz — fastest way to get hands-on with tensors, autograd, and neural networks.

Deep Learning with PyTorch: 60 Minute Blitz

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