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Power Up in 2023: Zero to Mastery with PyTorch for Deep Learning

Acquire PyTorch Skills, Become a Deep Learning Engineer, and Land Your Dream Job.

Created by- Andrei Neagoie, Daniel Bourke

What you’ll learn in PyTorch for Deep Learning

  • This course covers everything from the fundamentals of PyTorch to constructing real-world models.
  • Gain a comprehensive understanding of integrating Deep Learning into various tools and applications.
  • Develop and deploy your own custom-trained PyTorch neural network accessible to the public.
  • Elevate your expertise in deep learning, positioning yourself as a top candidate for recruiters seeking Deep Learning Engineers.
  • Acquire the essential skills required to thrive as a Deep Learning Engineer and potentially earn a salary of over US$100,000 per year.
  • Discover why PyTorch serves as an excellent starting point for entering the field of machine learning.
  • Learn to create and utilize machine learning algorithms, much like writing Python programs.
  • Harness the potential of data to construct machine learning algorithms that identify patterns and elevate your applications with AI capabilities.
  • Expand your skill set in Machine Learning and Deep Learning, enriching your toolkit for future projects.
PyTorch for Deep Learning

Course Content

  • Welcome to the Course
  • Understanding the Basics of PyTorch
  • Mastering the PyTorch Workflow
  • PyTorch for Neural Network Classification
  • Exploring Computer Vision with PyTorch
  • Handling Custom Datasets in PyTorch
  • Modular Approach in PyTorch
  • Transfer Learning Techniques in PyTorch
  • Tracking Experiments in PyTorch
  • Replicating Research Papers with PyTorch
  • Deploying PyTorch Models
  • Introduction to PyTorch 2.0 and torch.compile
  • Bonus Content
  • What’s Next: Further Advancements in PyTorch

Requirements

  • You will need a computer (Linux/Windows/Mac) with internet access for this course.
  • Some basic knowledge of Python is necessary to get started.
  • While prior Machine Learning knowledge is beneficial, it is not mandatory, as we offer ample supplementary resources to bring you up to speed without any difficulty.

Description

PyTorch for Deep Learning is a Python-based machine learning and deep learning framework that empowers users to develop and employ cutting-edge algorithms, including neural networks, which serve as the foundation for many of today’s Artificial Intelligence (AI) applications.

By learning PyTorch for Deep Learning, you gain the ability to create new and utilize existing state-of-the-art deep learning algorithms. This opens up opportunities to explore the vast potential of AI and leverage its capabilities to address real-world challenges and applications.

Moreover, PyTorch for Deep Learning is currently in high demand, resulting in an abundance of job opportunities across various industries.

Renowned companies, such as Tesla, utilize PyTorch for Deep Learning to construct computer vision systems for their self-driving cars, while Meta relies on it to empower their content timelines’ curation and understanding systems. Additionally, Apple employs PyTorch for Deep Learning to enhance computational photography techniques, further emphasizing its relevance and prominence in the tech world.

Want to know what’s even cooler?

What’s even more exciting is that PyTorch for Deep Learning plays a crucial role in the latest machine learning research, with many researchers choosing to publish their findings using PyTorch for Deep Learning code. By mastering PyTorchfor Deep Learning, you position yourself at the forefront of this highly sought-after and cutting-edge field.

Additionally, you’ll be joining a prestigious group of learners. Graduates of Zero To Mastery have gone on to secure positions at top tech companies like Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify, and others, who are pioneers in machine learning and deep learning. This indicates the significant impact and relevance of PyTorch in today’s rapidly evolving technological landscape.

This can be you.

Enroll today to experience the opportunity of joining our exclusive live online community classroom, where you can learn alongside thousands of fellow students, alumni, mentors, TAs, and Instructors.

The most significant advantage is that you will receive instruction in PyTorch for Deep Learning from a seasoned machine learning engineer with substantial real-world experience, ensuring you learn from one of the best teachers available!

What can you expect from this PyTorch for Deep Learning course?

Prepare for a highly engaging and practical learning experience as this PyTorch course is designed to be hands-on and project-based. Say goodbye to passive screen-staring often found in other PyTorch tutorials and courses.

Throughout the course, you’ll actively engage in the following:

  1. Running experiments: Gain firsthand experience by conducting experiments with PyTorch for Deep Learning, enhancing your understanding of its capabilities.
  2. Completing skill-testing exercises: Test and reinforce your comprehension of the material through interactive exercises.
  3. Building real-world deep learning models and projects: Tackle projects that simulate real-life scenarios, honing your ability to create practical solutions.

⚠ Important Note: This course is designed to be comprehensive, covering a wide range of topics. However, there’s no need to feel intimidated. Daniel will guide you through every step, starting from scratch, ensuring you grasp each concept with ease and confidence!

In this PyTorch course, you will learn the following:

  1. PyTorch Fundamentals: Starting from the basics, even if you’re a beginner, you’ll swiftly grasp essential concepts. You’ll dive into the world of tensors, the fundamental data representation in machine learning, and gain in-depth knowledge of PyTorch tensor datatype.
  2. PyTorch Workflow: Once you’ve mastered the fundamentals and know how to represent data as tensors, you’ll progress to understand the process of converting data into trained neural network models. This workflow will be your foundation throughout the course and beyond.
  3. PyTorch Neural Network Classification: Classification is a fundamental machine learning problem, and you’ll delve into it deeply. You’ll learn to code neural network classification models in PyTorch, allowing you to classify various items and answer essential questions like spam vs. non-spam emails, fraud detection in credit card transactions, and more.
  4. PyTorch Computer Vision: Witness how neural networks have revolutionized computer vision. Notably, PyTorch is driving cutting-edge advancements in computer vision algorithms. You’ll build a PyTorch neural network capable of recognizing patterns in images and classifying them into distinct categories. For instance, Tesla leverages PyTorch to develop the computer vision algorithms for their self-driving software.
  5. Pytorch Custom Datasets: the real magic of machine learning unfolds as you delve into constructing algorithms that unearth patterns within custom data.. While there are numerous existing datasets, you’ll learn how to load your own custom dataset into PyTorch. Within the PyTorch Custom Datasets section, you will explore the process of loading an image dataset for FoodVision Mini, a proficient PyTorch computer vision model capable of classifying images featuring pizza, steak, and sushi. This hands-on experience will undoubtedly intrigue your appetite for learning!
  6. PyTorch Going Modular: PyTorch’s for Deep Learning essence lies in its ability to facilitate Pythonic machine learning code. You’ll explore two main tools for writing machine learning code with Python: Jupyter/Google Colab notebooks, ideal for experimentation, and Python scripts, perfect for reproducibility and modularity. In this section, you’ll learn the art of transforming your essential Jupyter/Google Colab notebook code into reusable Python scripts—a common practice for sharing PyTorch code in the community.
  7. PyTorch Transfer Learning: Discover the power of transfer learning, where you leverage knowledge gained by one model to solve your unique problems. This section introduces you to the concept of transfer learning, allowing you to modify a machine learning model trained on vast image datasets to enhance the performance of FoodVision Mini, ultimately saving valuable time and resources.
  8. PyTorch Experiment Tracking: In Part 1 of the course’s Milestone Project, you’ll advance to the next level of machine learning by building numerous PyTorch models. To optimize your results, you need an organized system for comparing different FoodVision Mini experiments. PyTorch for Deep Learning Experiment Tracking will equip you with the tools to keep track of various experiment outcomes and identify the best-performing models.
  9. PyTorch Paper Replicating: In the rapidly evolving field of machine learning, new research papers surface regularly. Understanding and replicating these papers is an essential skill for staying at the forefront. In this section, you’ll learn to read and comprehend machine learning research papers and subsequently replicate them using PyTorch for Deep Learning code. As a significant milestone, you will embark on Part 2 of the project, involving the replication of the revolutionary Vision Transformer architecture.
  10. PyTorch Model Deployment: At this stage, your FoodVision model performs admirably. However, you’ve been the sole beneficiary of its capabilities so far. In this section, you’ll explore PyTorch for Deep Learning Model Deployment, which empowers you to share your model’s capabilities with others, making it accessible and usable by a broader audience.

How can you make your PyTorch models accessible to others?

In the final phase of your Milestone Project, you’ll tackle PyTorch for Deep Learning Model Deployment, which addresses the crucial question of making your PyTorch for Deep Learning models accessible to others. Specifically, in Part 3 of the project, you’ll learn the process of taking the best-performing FoodVision Mini model and deploying it to the web. This deployment will enable other users to access the model and test it with their own food images, providing them with a hands-on experience of your remarkable creation.

What’s the bottom line?

The field of machine learning is experiencing explosive growth and widespread adoption, and deep learning is the key to elevating your machine learning expertise to the next level. The demand for individuals with specialized deep learning knowledge is steadily increasing, with an array of job opportunities seeking these skills.

PyTorch, the framework embraced by industry giants like Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb, and many others, lies at the heart of cutting-edge advancements in the field.

Embrace the opportunity to embark on an exceptional learning journey with the most comprehensive online bootcamp tailored to mastering PyTorch. By enrolling in this course, you can confidently launch your career as a skilled Deep Learning Engineer, poised to make a remarkable impact in the fast-evolving world of deep learning.

Act now! Master PyTorch for Deep Learning and elevate your career with deep learning, unlocking new possibilities for higher salaries and rewarding opportunities. Enroll today!

Who will benefit from this course

  1. Aspiring Deep Learning Engineers: Anyone seeking a step-by-step guide to mastering PyTorch for Deep Learning and landing high-paying roles as Deep Learning Engineers, earning over $100,000 per year.
  2. Students, Developers, and Data Scientists: Individuals who wish to demonstrate practical machine learning expertise by building and training real models using PyTorch.
  3. AI and Machine Learning Enthusiasts: Those looking to expand their knowledge and skillset in AI, Machine Learning, and Deep Learning.
  4. Bootcamp Graduates and Tutorial Learners: Graduates of bootcamps or online PyTorch tutorials eager to go beyond the basics and acquire advanced skills.
  5. Students Seeking Real-World Practice: Learners who feel frustrated with beginner PyTorch for Deep Learning tutorials lacking real-world practice and essential skills needed for employment. This course offers the practical expertise required to stand out and get hired.

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Frequently Asked Questions (FAQs)

Q: What will I learn in PyTorch for Deep Learning?

A: You’ll gain a comprehensive understanding of PyTorch for Deep Learning fundamentals, deep learning integration, building custom-trained neural networks, and deploying models. The course covers computer vision, handling custom datasets, transfer learning, and replicating research papers using PyTorch.

Q: Who will benefit from this course?

A: Aspiring Deep Learning Engineers seeking step-by-step guidance, Students, Developers, and Data Scientists eager to demonstrate practical machine learning skills. AI and Machine Learning Enthusiasts looking to expand their knowledge, Bootcamp Graduates and Tutorial Learners seeking advanced skills, and Students in search of real-world practice and essential employable skills.

Q: Is prior Machine Learning knowledge required for this course?

A: No, prior Machine Learning knowledge is beneficial but not mandatory. The course provides ample supplementary resources to bring learners up to speed.

Q: How will the course help me with real-world applications?

A: The course is hands-on and project-based, enabling learners to run experiments, complete exercises, and build real-world deep learning models. It equips you with practical skills to tackle real-life scenarios and challenges.

Q: Why is PyTorch a preferred framework for deep learning?

A: PyTorch for Deep Learning is Python-based and empowers users to create and use state-of-the-art deep learning algorithms. Its versatility and widespread adoption by companies like Tesla, Microsoft, and others make it a sought-after tool in the field.

Q: Can I deploy my PyTorch models for others to use?

A: Yes, the course covers PyTorch Model Deployment, allowing you to make your models accessible to others, enhancing your portfolio and experience.

Q: Is the course suitable for complete beginners?

A: Yes, the course starts with PyTorch for Deep Learning fundamentals, making it accessible even to beginners with no prior experience in deep learning.

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