Learn PyTorch: The Free 26-Hour Masterclass Taking You From Beginner to Deep Learning Expert

Deep learning is revolutionizing domains from computer vision to natural language processing, and PyTorch is the driving force behind much of that transformation. According to the 2022 State of ML and Data Science Survey, PyTorch is now the most widely used deep learning framework among data science professionals, edging out TensorFlow by 5 percentage points.

What accounts for PyTorch‘s meteoric rise since its 2016 release? Its seamless Python integration, dynamic computational graphs, and intuitive API have endeared it to researchers and engineers alike. The result is a uniquely flexible and productive environment for iterating on novel architectures at the frontier of algorithmic innovation.

Harnessing that power, however, requires nontrivial expertise – expertise you‘ll find in spades in Daniel Bourke‘s "Learn PyTorch for Deep Learning" video course. Clocking in at a hefty 26 hours, this freely-available masterclass doesn‘t merely introduce PyTorch; it imbues you with a comprehensive framework for developing and deploying state-of-the-art deep learning solutions.

The 60,000 Foot View

Before zooming in on particulars, let‘s contextualize our journey. The overarching goal is to cultivate real-world, full-stack deep learning capabilities rooted in hands-on PyTorch mastery. Drawing on his experience as a professional machine learning engineer, Daniel structures the course around the actual skills and workflow you‘ll employ on the job:

1. Preparing data for ingestion by PyTorch models
2. Defining model architectures using PyTorch‘s declarative API
3. Training models and assessing their performance on validation data
4. Debugging, profiling, and optimizing model execution
5. Packaging and deploying trained models in production environments

While many tutorials content themselves with toy examples, this course trains you in the full lifecycle of industrial-strength model development. That‘s a powerful, rare, and commercially valuable skill set, whether you‘re aiming to boost your employability or tackle a moonshot startup idea.

The Curriculum

Concretely, "Learn PyTorch for Deep Learning" spans five dense modules, each culminating in a graded assignment to solidify your understanding. Let‘s survey them in turn:

00 – PyTorch Fundamentals

We hit the ground running with an exposure to PyTorch‘s bedrock abstraction: the tensor. Realizing the tensor as a multidimensional array, we‘ll manipulate them with PyTorch‘s comprehensive suite of slicing, indexing, and mathematical operations.

Crucially, we‘ll develop a computational graph mental model, interpreting tensor operations as defining abstract data flows to be optimized and compiled behind the scenes. On a first pass, reasoning about PyTorch programs in this dataflow style can be counterintuitive, but it‘s key to unlocking peak performance. Hands-on visualization exercises will reify these abstractions.

01 – PyTorch Workflow

Graduating from isolated tensors, we‘ll now weave them into coherent deep learning workflows. The star of the show is PyTorch‘s autograd functionality, which imbues tensors with automatic differentiation, enabling effortless backpropagation.

With autograd in hand, we‘ll practice the core training loop of modern deep learning:

1. Defining a model as a composition of differentiable primitives
2. Iterating over batches of input data
3. Computing a loss function quantifying model performance
4. Invoking backwards to compute gradients of the loss w.r.t. model parameters
5. Updating model parameters to minimize the loss via gradient descent

This process of gradually sculpting model weights to fit a dataset is the essence of deep learning. We‘ll drill it into muscle memory with extensive practice.

02 – Neural Network Classification

Here‘s where the magic happens. We‘ll leverage our PyTorch fundamentals to train authentic neural networks on real classification tasks. A particular highlight is the spiral classification project, where we‘ll build and train a multilayer perceptron to disentangle intertwined spirals of red and blue points.

Watching abstractions meld into shockingly effective classifiers is an empowering experience. We‘ll complement it by digging into the underlying machinery, interpreting the learned weights and activations to cultivate a visceral understanding of how neural nets operate. Expect lots of "aha" moments here.

03 – Computer Vision

Applying our classification chops, we‘ll now venture into the world of computer vision to build powerful image recognition models. We‘ll begin with a crash course on convolutional neural networks (CNNs), the workhorse architecture behind most state-of-the-art vision systems.

Combining CNNs with PyTorch‘s high-level APIs, we‘ll train models to categorize images from canonical benchmarks like CIFAR-10 and ImageNet. In the process, we‘ll explore the rich ecosystem of pre-trained models in the PyTorch Hub, harnessing transfer learning to achieve impressive results with minimal training.

04 – Custom Datasets

In our capstone project, we‘ll break free of curated datasets to tackle our own custom image classification problem. Leveraging PyTorch‘s flexible dataset abstractions, we‘ll code up data pipelines to feed our model on a bespoke collection of pizza, steak, and sushi images.

This end-to-end walkthrough, dubbed FoodVision, unites everything we‘ve learned in a tour de force suitable for any portfolio. It‘s a powerful testament to how far you can get with nothing but PyTorch, Python, and a zest for experimentation.

Beyond the Classroom

Impressive as they are, these modules are just the beginning of your PyTorch journey. Perhaps the most valuable aspect of the course is how it equips you to continue learning and creating independently.

In particular, Daniel places a strong emphasis on navigating the official PyTorch documentation. This sprawling corpus is the definitive resource on all things PyTorch, from low-level primitives to cutting-edge research tools. By the end of the course, you‘ll be comfortably consulting it to expand your knowledge and troubleshoot real-world issues.

You‘ll also be well-prepared to dive into the thriving PyTorch open source community. With over 60,000 GitHub repos and 150,000 Stack Overflow questions tagged PyTorch, there‘s no shortage of projects to learn from and contribute to. Daniel frequently references popular codebases that demonstrate best practices, giving you a roadmap to deepen your expertise.

The PyTorch Payoff

Make no mistake: Investing in PyTorch skills will supercharge your machine learning career trajectory. With the popularity of deep learning exploding across industries, corporates and startups alike are scrambling to secure PyTorch talent. A quick LinkedIn search turns up over 70,000 job postings seeking PyTorch experience, spanning research, engineering, and product development roles.

And it‘s not just the volume of opportunities that stands out – it‘s their caliber. PyTorch proficiency qualifies you for some of the most impactful (and well-compensated) challenges in modern tech, from autonomous vehicles to protein structure prediction. Even a cursory scan of AI case studies reveals PyTorch as the substrate of countless breakthroughs:

• Tesla‘s Autopilot perception system relies on PyTorch to parse real-time sensor data and detect obstacles
• OpenAI‘s GPT-3 language model, powering AI writing tools like ChatGPT, was developed and trained in PyTorch
• DeepMind‘s AlphaFold 2 employed novel PyTorch architectures to solve the 50-year grand challenge of protein folding

The list goes on, encompassing startups and tech giants, research labs and product teams, computer vision and NLP and RL. The common denominator is PyTorch‘s unparalleled expressiveness and scalability, empowering practitioners to push the boundaries of the possible.

Get Started Today

Sold on PyTorch‘s potential but daunted by the learning curve? Don‘t be. Every deep learning master started as a beginner, and Daniel Bourke‘s "Learn PyTorch for Deep Learning" course is your ticket to an uncommonly smooth ascent.

With 26 hours of lovingly crafted modules, interactive Jupyter notebooks, and invaluable industry war stories, it‘s a peerless launchpad for your PyTorch career. Best of all, it‘s freely accessible to anyone with a web browser, courtesy of freeCodeCamp‘s indefatigable volunteers.

So what are you waiting for? Carve out some quality time, fire up the first video, and begin your transformation into a PyTorch powerhouse today. A world of opportunity awaits on the other side.