Are you ready to become a deep learning engineer? If so, look no further than the PyTorch for Deep Learning in 2023: Zero to Mastery course. With a course rating aggregate of 4.62825 and over 1000 course reviews, this course will take you from zero to hero in no time.
What is PyTorch and Why Should You Learn it?
PyTorch is a deep learning and machine learning framework written in Python. It allows you to create and use state-of-the-art deep learning algorithms and neural networks to power many of today's artificial intelligence applications. This framework is so hot right now, meaning there are plenty of jobs available for those who learn it.
Companies such as Tesla, Meta, and Apple use PyTorch to power their systems for self-driving cars and computationally enhanced photography. By taking this course, you'll learn PyTorch from a professional machine learning engineer with over 20 years of experience who has worked with top tech companies such as Google, Amazon, and Uber. You'll also have the opportunity to join an exclusive online community classroom to learn alongside thousands of students, alumni, mentors, TAs, and instructors.
What Will This PyTorch Course Be Like?
This PyTorch course is incredibly hands-on and project-based. You won't just be staring at your screen; you'll actually be running experiments, completing exercises to test your skills, and building real-world deep learning models and projects to mimic real-life scenarios.
By the end of the course, you'll have the skills needed to identify and develop modern deep learning solutions that big tech companies encounter. The course is comprehensive, but don't be intimidated. The course material is taught from scratch and step-by-step.
What Will You Learn in this PyTorch Course?
1. PyTorch Fundamentals
In this section, you'll start with the barebone fundamentals of PyTorch. Even if you're a beginner, you'll get up to speed quickly. You'll learn how to craft PyTorch tensors, which are a collection of numbers that represent data in machine learning. PyTorch Fundamentals will cover the PyTorch tensor datatype in-depth.
2. PyTorch Workflow
Now that you've got the fundamentals down and made some tensors, you'll learn the next steps to go from data to trained neural network model in PyTorch Workflow. These steps are foundational in PyTorch code and will be used throughout the course.
3. PyTorch Neural Network Classification
Classification is one of the most common machine learning problems, and in this section, you'll learn how to code a neural network classification model using PyTorch so that you can classify things and answer questions like, "Is something one thing or another?" or "Is an email spam or not spam?"
4. PyTorch Computer Vision
Neural networks have changed the game of computer vision forever. In this section, PyTorch drives many of the latest advancements in computer vision algorithms. You'll learn how to build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories. Tesla uses PyTorch to build the computer vision algorithms for their self-driving software, making this section particularly relevant and exciting.
5. PyTorch Custom Datasets
The magic of machine learning is finding patterns in custom data. In this section, you'll learn how to load an image dataset into PyTorch for FoodVision Mini, a PyTorch computer vision model specialized to classify images of pizza, steak, and sushi.
6. PyTorch Going Modular
The whole point of PyTorch is to write Pythonic machine learning code. You'll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it into reusable Python scripts in this section, which is often how you'll find PyTorch code shared in the wild.
7. PyTorch Transfer Learning
Transfer learning is a powerful tool in machine learning that allows you to take what one model has learned and leverage it for your problems. This section will cover the power of transfer learning and show you how to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision mini, ultimately saving you time and resources.
8. PyTorch Experiment Tracking
Now it's time to start cooking with heat and start Part 1 of the Milestone Project of the course. You'll have built plenty of PyTorch models by this point, but how do you keep track of which model performs the best? PyTorch Experiment Tracking comes to the rescue with a system that allows you to keep track of various FoodVision Mini experiment results and compare them to find the best.
9. PyTorch Paper Replicating
In this section, you'll learn how to go through a machine learning research paper and replicate it with PyTorch code. You'll undertake Part 2 of the Milestone Project, where you'll replicate the groundbreaking Vision Transformer architecture. With the help of this course, you'll become proficient in understanding machine learning research papers.
10. PyTorch Model Deployment
In this section, you'll learn how to take the best performing FoodVision Mini model, and deploy it to the web so others can access and try it out with their food images. Part 3 of the Milestone Project requires you to deploy the model, making this section overtly practical and useful.
Bottom Line
As the field of machine learning and deep learning grows and evolves, the demand for specialized knowledge in PyTorch is growing. By taking this course, you'll have access to a comprehensive and hands-on program that will take you from zero to mastery, enabling you to become a deep learning engineer. Companies such as Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb, and many others are currently powered by PyTorch, making this course a surefire opportunity to enhance your career and earn a higher salary.
So why wait? Kickstart your deep learning career with PyTorch for Deep Learning in 2023: Zero to Mastery.
