PyTorch for Everyone: Everything I know and More.
The story begins on one of those May days when the wind is still cold, but the sun shines promisingly, hinting at the forthcoming summer. Me and my colleague Joseph were taking a walk through one of many elaborate gardens of our office. Being less cautious than Joseph, I found myself frequently struck by low-hanging tree branches. Meanwhile, Joseph was struck by amazing ideas and challenges that he discussed with me. One of those challenges was rather interesting: How could we teach someone all the PyTorch fundamentals, starting from tensors and extending to convolutional neural networks, in less than 24 hours?
Developed by Meta AI (basically Facebook), PyTorch provides a high-level interface for building and training deep learning models. It has emerged as one of the most popular frameworks for deep learning, renowned for its flexibility and ease of use. Researchers and developers use it to build and train complex neural networks with remarkable efficiency.
Even ChatGPT is built on top of PyTorch.
It allows you to express your ideas in code fluidly, enabling rapid experimentation and prototyping.
The goal was set, the execution was almost flawless and now I present to you the product of our work, blood and tears: PyTorch Learning Path
You can find the whole learning path here:
This Learning Path consists of 5 courses and 2 guided projects. It is a summary of everything I know and a small fraction of what Joseph knows about PyTorch and ML in general. Each course is important and valuable, and combined together they shouldn’t take more than 24 hours to pass. Through a series of lectures and hands-on exercises, you will gain a deep understanding of PyTorch and develop practical skills to apply your knowledge to real-life problems. It not only covers the basics of PyTorch for machine learning but also provides explanations on how to build deep learning models using PyTorch. Additionally, it delves into the theoretical fundamentals of machine learning, such as back-propagation and cost functions.
Let’s delve into the details:
1: PyTorch Tensor, Dataset and Data Augmentation
Data preparation plays a crucial role in effectively solving machine learning problems. PyTorch, a powerful deep learning framework, offers a plethora of tools to make data loading easy. The PyTorch Tensor, Dataset, and Data Augmentation Fundamentals course will give a solid understanding of the basics and core principles of PyTorch, specifically focusing on tensor manipulation, dataset management, and data augmentation techniques.
This course is free and published, you can access it by clicking here:
2: Linear Regression with PyTorch
This course is designed to provide you with a comprehensive understanding of linear regression modeling using the PyTorch framework. Equipped with these skills, you will be prepared to tackle real-world regression problems and utilize PyTorch effectively for predictive analysis tasks. It focuses specifically on the implementation and practical application of linear regression algorithms for predictive analysis.
This course is free and published, you can access it by clicking here:
3: Classification with PyTorch
Designed for students and enthusiasts, this course equips you with the knowledge and practical skills to build powerful and accurate classification models using PyTorch. It offers a hands-on learning experience, allowing you to apply your knowledge through coding exercises and lessons so by the end of the course, you will possess the skills to build, train, and evaluate classification models using PyTorch.
This course is free and published, you can access it by clicking here:
4: Build a Neural Network with PyTorch
In this course, you will be focusing on how PyTorch creates and optimizes Neural Network models. We will quickly iterate through different aspects of PyTorch Neural Networks, giving you strong foundations and all the prerequisites you need to build deep learning models. Designed for students and professionals interested in machine learning and deep learning, this course offers a comprehensive understanding of the theory and practical applications of building and deploying neural networks.
This course is published, and you can access it by clicking here:
5: Convolutional Neural Networks with PyTorch
In this course you will gain practical skills to tackle real-world image analysis and computer vision challenges using PyTorch. Uncover the power of Convolutional Neural Networks (CNNs) and explore the fundamentals of convolution, max pooling, and convolutional networks. Learn to train your models with GPUs and leverage pre-trained networks for transfer learning.
This course is published, and you can access it by clicking here:
All you need to start learning is some basic understanding of Python for programming and school level math for theory behind ML concepts.
Note that since it’s a Learning Path, It follows a sequential structure, requiring completion or a solid understanding of the concepts covered in the previous courses before progressing to the next. For instance, to begin the “Classification with PyTorch” course, the prerequisites include:
- Completion of PyTorch: Tensor, Dataset and Data Augmentation course
- Completion of Linear Regression with PyTorch course
or
Good understanding of PyTorch Tensors, DataSets and Linear Regression
We have made those courses as a small textbook for ourself and others on ML and PyTorch basics and we also wanted to share it with the public for others to have a quick and easy access to this knowledge. You can find a lot of FREE courses and projects about data science or any other technology topics in Cognitive Class.
A Small Bonus:
In addition to the Course work, we have added 2 more projects for you to further practice and apply your PyTorch Skills. In the first project, you will be utilizing simple learning rules to “teach” a neural network how to recognize objects the images and solve the big question: hotdog or not hotdog? In the second project you will have a chance to launch an AI Hotdog Detector as a Serverless Python App.
