What is PyTorch ?
Python-based PyTorch is a framework for deep learning and machine learning.
With PyTorch, you may create new deep learning algorithms and utilize ones that already exist. Neural networks are the technology behind many of today’s Artificial Intelligence (AI) applications.
There are plenty of jobs available right now because it’s so hot as well!
Companies like: use PyTorch.
Tesla will create the computer vision systems for its autonomous vehicles.
They use meta to enable their content timelines’ curation and understanding processes.
Apple will develop superior photography using computing.
What can you expect from this PyTorch course?
The projects in this PyTorch course are very practical. You won’t simply be gazing at your computer screen. That’s something that additional PyTorch tutorials and courses should cover.
Your actual role in this training will be:
completing tests to gauge your abilities
constructing deep learning models and projects that accurately reflect real-world situations
By the end of it all, you’ll be equipped with the knowledge and expertise required to recognize and create cutting-edge deep learning solutions for Big Tech businesses.
What you will study in this PyTorch course is as follows:
- PyTorch Fundamentals
— We start with the rudimentary fundamentals so that even a newbie can catch up.
The representation of data in machine learning is a tensor (a collection of numbers). Building machine learning algorithms requires mastering PyTorch tensor creation. The PyTorch tensor datatype is thoroughly covered in PyTorch Fundamentals.
. PyTorch Neural Network Classification
– One of the most prevalent machine learning issues is classification.
Is something either this or that?
Is a particular email spam or not?
Is using a credit card fraudulent or not?
You may classify items and get the answers to these questions by utilizing PyTorch Neural Network Classification, which teaches you how to design a neural network classification model.
. PyTorch Computer Vision – The use of neural networks has completely altered the field of computer vision. The majority of the most recent developments in computer vision algorithms are now powered by PyTorch.
To create the computer vision algorithms for their self-driving software, Tesla, for instance, uses PyTorch.
You may create a PyTorch neural network that can recognize patterns in photos and classify them using PyTorch Computer Vision
- PyTorch Transfer Learning
– What if you could use what one model has discovered to solve the issues you face? PyTorch Transfer Learning addresses these topics.
Learn how a machine learning model based on millions of photographs may be significantly modified to improve the performance of FoodVision Mini while saving you time and money. This is the power of transfer learning.
- PyTorch Experiment Tracking
— We are about to begin Part 1 of our course’s Milestone Project, in which we will begin cooking with heat.
You will have constructed many PyTorch models by this stage. But how can you monitor which model delivers the best results?
PyTorch Experiment Tracking can help with that.
Following the credo of a machine learning practitioner,
- PyTorch Paper Replicating
– Machine learning is a field that develops swiftly. Every day, new research articles are published. It takes time and practice to be able to read and comprehend these documents.
That is the subject matter covered by PyTorch Paper Replicating. You’ll discover how to use PyTorch code to duplicate a machine learning research article.
You will now start working on Part 2 of our Milestone Project, in which you will copy the innovative Vision Transformer architecture!
- PyTorch Model Deployment – By now, your FoodVision model should be operating effectively. But you’ve been the only one with access to it until now.
- PyTorch Workflow
– Alright, so you understand the foundations and have created some tensors to represent data. What comes next?
You may learn the procedures for moving from data to tensors to trained neural network models using PyTorch Workflow. These procedures will be present and used throughout the remainder of the course and every time you see PyTorch code.