PyTorch for Deep Learning Bootcamp from Udemy

What is PyTorch and why should I learn it?

PyTorch is a machine learning and deep learning framework written in Python.

PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks powering much of today’s Artificial Intelligence (AI) applications.

Plus it's so hot right now, so there's lots of jobs available.

PyTorch is used by companies like:

Tesla to build the computer vision systems for their self-driving cars
Meta to power the curation and understanding systems for their content timelines
Apple to create computationally enhanced photography.

Want to know what's even cooler?

Much of the latest machine learning research is done and published using PyTorch code so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.

And you'll be learning PyTorch in good company.

Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify + other top tech companies at the forefront of machine learning and deep learning.

This can be you.

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you will be learning PyTorch from a professional machine learning engineer, with real-world experience, and who is one of the best teachers around.

What will this PyTorch course be like?

This PyTorch course is very hands-on and project based. You won't just be staring at your screen. We'll leave that for other PyTorch tutorials and courses.

In this course you'll actually be:

Running experiments
Completing exercises to test your skills
Building real-world deep learning models and projects to mimic real life scenarios

By the end of it all, you'll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter.

Fair warning: this course is very comprehensive. But don't be intimidated, Daniel will teach you everything from scratch and step-by-step.

Here's what you'll learn in this PyTorch course:

1. PyTorch Fundamentals — We start with the barebone fundamentals, so even if you're a beginner you'll get up to speed.

In machine learning, data gets represented as a tensor (a collection of numbers). Learning how to craft tensors with PyTorch is paramount to building machine learning algorithms. In PyTorch Fundamentals we cover the PyTorch tensor datatype in-depth.

2. PyTorch Workflow — Okay, you’ve got the fundamentals down, and you've made some tensors to represent data, but what now?

With PyTorch Workflow you’ll learn the steps to go from data -> tensors -> trained neural network model. You’ll see and use these steps wherever you encounter PyTorch code as well as for the rest of the course.

3. PyTorch Neural Network Classification — Classification is one of the most common machine learning problems.

Is something one thing or another?
Is an email spam or not spam?
Is credit card transaction fraud or not fraud?

With PyTorch Neural Network Classification you’ll learn how to code a neural network classification model using PyTorch so that you can classify things and answer these questions.

4. PyTorch Computer Vision — Neural networks have changed the game of computer vision forever. And now PyTorch drives many of the latest advancements in computer vision algorithms.

For example, Tesla use PyTorch to build the computer vision algorithms for their self-driving software.

With PyTorch Computer Vision you’ll build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories.

5. PyTorch Custom Datasets — The magic of machine learning is building algorithms to find patterns in your own custom data. There are plenty of existing datasets out there, but how do you load your own custom dataset into PyTorch?

This is exactly what you'll learn with the PyTorch Custom Datasets section of this course.

You’ll learn how to load an image dataset for FoodVision Mini: a PyTorch computer vision model capable of classifying images of pizza, steak and sushi (am I making you hungry to learn yet?. ).

We’ll be building upon FoodVision Mini for the rest of the course.

6. PyTorch Going Modular — The whole point of PyTorch is to be able to write Pythonic machine learning code.

There are two main tools for writing machine learning code with Python:

A Jupyter/Google Colab notebook (great for experimenting)
Python scripts (great for reproducibility and modularity)

In the PyTorch Going Modular section of this course, you’ll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it reusable Python scripts. This is often how you’ll find PyTorch code shared in the wild.

7. PyTorch Transfer Learning — What if you could take what one model has learned and leverage it for your own problems? That’s what PyTorch Transfer Learning covers.

You’ll learn about the power of transfer learning and how it enables you to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision Mini, saving you time and resources.

8. PyTorch Experiment Tracking — Now we're going to start cooking with heat by starting Part 1 of our Milestone Project of the course.

At this point you’ll have built plenty of PyTorch models. But how do you keep track of which model performs the best?

That’s where PyTorch Experiment Tracking comes in.

Following the machine learning practitioner’s motto of experiment, experiment, experiment. you’ll setup a system to keep track of various FoodVision Mini experiment results and then compare them to find the best.

9. PyTorch Paper Replicating — The field of machine learning advances quickly. New research papers get published every day. Being able to read and understand these papers takes time and practice.

So that’s what PyTorch Paper Replicating covers. You’ll learn how to go through a machine learning research paper and replicate it with PyTorch code.

At this point you'll also undertake Part 2 of our Milestone Project, where you’ll replicate the groundbreaking Vision Transformer architecture.

10. PyTorch Model Deployment — By this stage your FoodVision model will be performing quite well. But up until now, you’ve been the only one with access to it.

How do you get your PyTorch models in the hands of others?

That’s what PyTorch Model Deployment covers. In Part 3 of your Milestone Project, you’ll learn how to take the best performing FoodVision Mini model and deploy it to the web so other people can access it and try it out with their own food images.

What's the bottom line?

Machine learning's growth and adoption is exploding, and deep learning is how you take your machine learning knowledge to the next level. More and more job openings are looking for this specialized knowledge.

Companies like Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb and many others are currently powered by PyTorch.

And this is the most comprehensive online bootcamp to learn PyTorch and kickstart your career as a Deep Learning Engineer.

So why wait? Advance your career and earn a higher salary by mastering PyTorch and adding deep learning to your toolkit?

What's inside

Syllabus

Introduction

PyTorch for Deep Learning

Course Welcome and What Is Deep Learning

Join Our Online Classroom!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Starts with PyTorch fundamentals, which ensures that even beginners can grasp the core concepts and build a solid foundation in deep learning

Covers PyTorch workflow, which is essential for understanding how to move from data to trained neural network models, a crucial skill for practical applications

Explores PyTorch computer vision, which allows learners to build neural networks capable of classifying images, a skill used in self-driving cars and other applications

Teaches how to create custom datasets, which enables learners to apply PyTorch to their own unique problems and datasets, enhancing practical skills

Includes PyTorch model deployment, which teaches learners how to make their models accessible to others via the web, a critical step for real-world impact

Features PyTorch paper replicating, which helps learners understand and implement the latest machine learning research, keeping them at the cutting edge of the field

Reviews summary

Practical pytorch for deep learning

According to learners, the "PyTorch for Deep Learning Bootcamp" is a comprehensive and highly practical course focused on getting students building with PyTorch. Students say the hands-on approach, featuring numerous coding exercises and projects, is a major strength, helping solidify understanding of complex topics. Many found the instructor's explanations clear and engaging, making advanced deep learning concepts accessible. Reviewers highlight the course's coverage of essential topics from fundamentals to transfer learning and deployment as particularly valuable for those aiming for a career in the field. While the course is thorough, a few reviewers suggest it can be challenging for absolute beginners without some prior Python or machine learning exposure.

Aligns with skills needed for deep learning jobs.

"This course provides the practical skills companies are looking for in deep learning engineers."

"Learning deployment and experiment tracking is highly relevant for a career in this field."

"The focus on building complete projects is excellent preparation for industry roles."

"It definitely feels like a bootcamp designed to get you job-ready."

Learn by building practical deep learning models.

"Building projects like FoodVision Mini really helped consolidate my learning."

"The milestone project structure is great for applying everything learned throughout the course."

"Projects are realistic and give a good sense of how to approach real-world problems with PyTorch."

"I gained confidence by completing the coding projects."

Instructor explains complex concepts clearly.

"The instructor is excellent at explaining complex topics in a clear and understandable way."

"Lectures are well-structured and easy to follow, even when the concepts are difficult."

"His teaching style makes deep learning much less intimidating than I expected."

"I found the explanations particularly helpful in grasping the intuition behind the code."

Covers a wide range of essential PyTorch topics.

"This bootcamp really covers everything you need to get started with PyTorch, from tensors to deployment."

"I appreciate the breadth of topics covered, including transfer learning and replicating research papers."

"The syllabus is extensive and delivers on its promise of a comprehensive look at PyTorch for DL."

"It provides a solid foundation in PyTorch fundamentals and builds up to more advanced applications."

Focuses heavily on coding and building projects.

"The course is extremely practical and hands-on, which is exactly what I needed to learn PyTorch."

"I loved the emphasis on building real projects and writing code from scratch. It made the concepts stick."

"Instead of just theory, we're constantly applying what we learn through exercises and projects. Very effective."

"The hands-on coding and projects are the strongest part of the course for me, helping me apply theory."

Requires some prior background for best results.

"While it says it starts from scratch, I think some prior Python and ML knowledge is really helpful."

"Absolute beginners might find the pace challenging in later sections."

"It's a bootcamp, so expect it to move quickly. Be prepared to pause and re-watch."

"I had some ML background, which made following the content much easier."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch for Deep Learning Bootcamp with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra concepts, which are fundamental to understanding tensors and matrix operations in PyTorch.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, multiplication, and transposition.
Study vector spaces, linear independence, and basis vectors.
Practice solving systems of linear equations.

Review 'Deep Learning with PyTorch' by Eli Stevens, Luca Antiga, and Thomas Viehmann

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Gain a deeper understanding of PyTorch concepts and best practices by studying a dedicated book on the subject.

View Deep Learning with PyTorch: Build, train, and... on Amazon

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Read the chapters relevant to the current course modules.
Experiment with the code examples provided in the book.
Compare the book's approach to the course's approach.

Review 'Programming PyTorch for Deep Learning' by Ian Pointer

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Supplement your learning with a practical guide to PyTorch programming.

View Programming PyTorch for Deep Learning: Creating... on Amazon

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Work through the code examples in the book.
Adapt the examples to solve similar problems.
Compare the book's coding style to the course's coding style.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Blog Post Explaining Backpropagation

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Solidify your understanding of backpropagation by explaining it in a blog post format.

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Research and understand the mathematical foundations of backpropagation.
Write a clear and concise explanation of the backpropagation algorithm.
Include diagrams and examples to illustrate the concepts.
Publish the blog post on a platform like Medium or a personal website.

Implement Neural Network Architectures from Scratch

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Reinforce your understanding of neural networks by implementing various architectures from scratch using PyTorch.

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Implement a simple feedforward neural network.
Implement a convolutional neural network (CNN) for image classification.
Implement a recurrent neural network (RNN) for sequence modeling.
Compare the performance of different architectures on various datasets.

Create a Presentation on Transfer Learning

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Deepen your understanding of transfer learning by creating a presentation explaining the concept and its applications.

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Research different transfer learning techniques.
Prepare slides explaining the benefits and limitations of transfer learning.
Include examples of successful transfer learning applications.
Practice delivering the presentation to an audience.

Build an Image Classifier for a Custom Dataset

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Apply your PyTorch skills to build a real-world image classifier for a custom dataset.

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Choose a dataset of images to classify.
Preprocess the images and create a PyTorch dataset.
Design and train a CNN model using PyTorch.
Evaluate the model's performance and fine-tune it as needed.

Career center

Learners who complete PyTorch for Deep Learning Bootcamp will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

A Deep Learning Engineer builds and deploys deep learning models for various applications. This PyTorch course helps you build a strong foundation in PyTorch, a popular framework for deep learning. As a Deep Learning Engineer, you use tools like PyTorch to create neural networks that power AI applications. This course covers the fundamentals of PyTorch, including tensor manipulation, neural network classification, and computer vision using neural networks, enabling you to develop and deploy modern deep learning solutions. The hands-on, project-based approach makes it particularly valuable, teaching you how to run experiments and build real-world models. Topics such as transfer learning and model deployment help you to create machine learning solutions.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This PyTorch course helps you craft and implement state-of-the-art deep learning algorithms. As a Machine Learning Engineer, you use frameworks like PyTorch to build neural networks. This course covers PyTorch fundamentals, neural network classification, and computer vision, providing the necessary skills to develop deep learning solutions. Exposure to custom datasets, transfer learning, and deploying models to the web makes the course invaluable. Those who wish to be Machine Learning Engineers will develop the skills to identify patterns in data, such as creating a machine learning model to classify images of pizza, steak, and sushi.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

A Computer Vision Engineer develops algorithms that enable computers to see and interpret images. With this PyTorch course, you'll build a neural network capable of seeing patterns in images and classifying them into different categories. As a Computer Vision Engineer, you'll leverage neural networks and tools like PyTorch to improve computer vision abilities. The PyTorch Computer Vision section of this course is especially useful, and it teaches you how to build a PyTorch neural network to classify images. Furthermore, familiarity with custom datasets and transfer learning helps refine your ability to work with varied image data and improve model performance. The course helps build a foundation in PyTorch.

See salaries and explore the career path for Computer Vision Engineer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher explores and develops new AI algorithms and techniques. This PyTorch course helps you learn the practical skills needed to implement and test new algorithms. The course teaches you how to replicate research papers with PyTorch increasing your ability to understand and implement cutting-edge research. A critical part of being an Artificial Intelligence Researcher is undertaking experiments, and this course teaches you how to keep track of the experiment results to find the best. The knowledge of PyTorch fundamentals, neural network architectures, and model deployment makes this course particularly relevant, helping you to contribute meaningfully to the field of AI research.

See salaries and explore the career path for Artificial Intelligence Researcher

AI Software Developer

An AI Software Developer integrates AI models into software applications. This PyTorch course helps you learn how to deploy deep learning models using PyTorch. As an AI Software Developer, you need to create and integrate AI solutions into existing software systems. The PyTorch Model Deployment section of this course is invaluable, teaching you how to take a trained PyTorch model and deploy it to the web, making it accessible to others. Additionally, knowledge of PyTorch fundamentals, neural network architectures, and custom datasets helps you build and customize AI models to fit specific application requirements.

See salaries and explore the career path for AI Software Developer

AI Consultant

An AI Consultant advises organizations on how to implement AI solutions to business problems. This PyTorch course helps increase your understanding of the practical aspects of deep learning. As an AI Consultant, you will be able to guide clients on the feasibility and implementation of AI projects. The knowledge of PyTorch, neural networks, and model deployment helps you provide informed recommendations. The survey of PyTorch capabilities provided by this course supports your ability to align AI solutions with business needs.

See salaries and explore the career path for AI Consultant

Image Processing Specialist

An Image Processing Specialist manipulates and analyzes digital images. This PyTorch course may be useful as it helps you to develop computer vision algorithms using PyTorch. As an Image Processing Specialist, you could use these skills to enhance image quality, extract features, or identify objects within images. The section on computer vision provides hands-on experience with building neural networks for image classification. Furthermore, the knowledge of custom datasets and transfer learning allows you to adapt models to the specific characteristics of your image data.

See salaries and explore the career path for Image Processing Specialist

Data Scientist

A Data Scientist analyzes data to extract meaningful insights and develop data-driven solutions. This PyTorch course may be useful for Data Scientists, particularly those working with unstructured data like images or text, and looking to utilize deep learning techniques. As a Data Scientist, you will benefit from learning how to build and train neural networks using PyTorch. The sections on PyTorch fundamentals, neural network classification, and custom datasets provide the skills needed to develop predictive models and extract insights from complex data. Additionally, learning how to deploy PyTorch models to the web can enable you to share your findings and models with a wider audience.

See salaries and explore the career path for Data Scientist

Research Scientist

A Research Scientist conducts scientific research. This PyTorch course may be useful if your research involves machine learning or deep learning. As a Research Scientist, you'll likely need to implement and test novel algorithms, and this course teaches you how to replicate research papers using PyTorch. The exposure to transfer learning, experiment tracking, and model deployment may streamline your research workflow, and assist in publishing high-quality work. You should take this course to understand how to stay at the cutting edge of a highly in-demand field.

See salaries and explore the career path for Research Scientist

Algorithm Developer

An Algorithm Developer designs and implements algorithms for various applications. This PyTorch course may be useful as it helps you to develop and optimize deep learning algorithms. As an Algorithm Developer, you'll be able to use PyTorch to create neural networks that solve complex problems. The course covers the fundamentals of PyTorch, neural network classification, and computer vision. The focus on experiment tracking and paper replication will help you refine your ability to design and implement advanced algorithms.

See salaries and explore the career path for Algorithm Developer

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots. This PyTorch course may be useful, especially if you are working on robots that use computer vision or other AI-driven perception systems. As a Robotics Engineer, you will be using AI to enable robots to understand and interact with their environment. The PyTorch Computer Vision section of this course teaches you how to build neural networks to see and classify images. Furthermore, skills in custom datasets and transfer learning can help you adapt models to the specific needs of your robotic systems. Tesla uses PyTorch to build computer vision systems for self-driving, which is relevant for Robotics.

See salaries and explore the career path for Robotics Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software systems. Learning PyTorch may be useful for Software Engineers interested in integrating AI functionalities into their applications. This course covers the fundamentals of PyTorch, neural network classification, and computer vision applications. The focus on modular coding and model deployment can help you to integrate deep learning components into larger software systems. The knowledge of how to leverage transfer learning can also save time and resources when building AI-powered features.

See salaries and explore the career path for Software Engineer

Data Engineer

A Data Engineer builds and maintains the infrastructure for data storage and processing. This PyTorch course may be useful for Data Engineers who need to support machine learning workflows. As a Data Engineer, you'll benefit from the insights into machine learning model deployment and the handling of custom datasets. The knowledge of how data flows from raw input to trained models allows you to optimize data pipelines and infrastructure for machine learning applications. The overview of PyTorch fundamentals helps you understand the framework used by data scientists and machine learning engineers.

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Data Analyst

A Data Analyst examines and interprets data to identify trends and patterns. This PyTorch course may be useful for a Data Analyst as it introduces you to deep learning techniques that can improve data analysis. As a Data Analyst, the ability to leverage neural networks for classification and prediction can enhance your analytical capabilities. Although the course focuses on deep learning models, the familiarity it provides with data representation and manipulation in PyTorch may enable you to extract more actionable insights from data.

See salaries and explore the career path for Data Analyst

Natural Language Processing Engineer

A Natural Language Processing Engineer develops algorithms that enable computers to understand and process human language. While this course focuses on computer vision, the fundamental PyTorch skills you gain may be useful. As a Natural Language Processing Engineer, you can apply these skills to create models for sentiment analysis, language translation, and text generation. The sections on PyTorch fundamentals and neural network architectures establish a foundation for working with sequence data and building NLP models. The modular and reproducible coding practices taught in the course are valuable for developing robust and scalable NLP solutions.

See salaries and explore the career path for Natural Language Processing Engineer

PyTorch for Deep Learning Bootcamp

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Reviews summary

Practical pytorch for deep learning

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