Download PyTorch for Deep Learning

What you would learn in PyTorch for Deep Learning course?

PyTorch is a machine-learning as well as a deep-learning framework that is developed in Python.

PyTorch lets you create the latest and most advanced deep learning algorithms, such as neural networks that power many current Artificial Intelligence (AI) applications.

Additionally, it's super hot at the moment, so there is plenty of work available!

PyTorch is utilized by businesses such as:

• Tesla is building computer vision systems that will be used in their self-driving vehicles
• Meta to support curation and understanding systems that support their content timelines
• Apple to create enhanced computational photography.

Are you curious about what's cooler?

Most current machine learning studies are conducted and published with PyTorch code. Knowing how it works will allow you to be on the cutting edge of this highly sought-after field.

The PyTorch course is practical and project-based. It's not just a matter of looking at your computer screen; we'll save that for future PyTorch tutorials and classes.

In this course, you'll be:

• Experiments in running
• You can complete tests to measure your abilities
• Models that are based on real-world data and projects that replicate real-world scenarios

When you're done with everything, you'll have the necessary skills to spot and design modern deep-learning techniques which Big Tech companies encounter.

Course Content:

1. PyTorch Fundamentals The first step is the basics, which means you'll soon be up to speed even if you're not a professional.

Regarding machine learning, information is expressed as a Tensor (a set of numbers). Understanding how to create tensors using PyTorch is essential to creating algorithmic machine learning. In PyTorch Fundamentals, we will cover the PyTorch Tensor datatype in depth.

2. PyTorch Workflow: Okay, you've learned the basics and created some tensors that represent data, but what do you do next?

With PyTorch Workflow, you'll be taught the steps needed to move from data to Tensors to a neural model that has been trained. You'll find these methods wherever you encounter PyTorch code and throughout the course.

3. PyTorch Neural Network Classification -Classification is among the most frequently encountered problems in machine learning.

• Is it an item of a different kind?
• Does an email count as spam, or is it not?
• Is a credit card transaction legitimate or not?

Through PyTorch Neural Network Classification, you'll be able to create an algorithm for neural network classification by using PyTorch to categorize things and answer these questions.

4. PyTorch Computer Vision Neural networks have revolutionized the field of computer vision forever, and they are now PyTorch powers many of the most recent advancements for computer vision.

For instance, Tesla uses PyTorch to develop computer vision algorithms for their autonomous driving software.

Using PyTorch Computer Vision, you'll construct a PyTorch neural network capable of identifying patterns in images and separating them into different types.

5. PyTorch Custom Datasets Machine learning is the creation of algorithms that discover patterns in your custom data. Many existing datasets are available, But how do you import your customized dataset into PyTorch?

That's precisely what you'll learn in this course's PyTorch Custom Datasets section.

You'll be taught what to do when loading an image data to FoodVision Mini: a PyTorch computer vision model capable of categorizing images of pizza, steak, sushi, and other foods (am I making the feel hungry to learn Are you hungry? ).

We'll build on FoodVision Mini for the rest of the class.

6. PyTorch Moving Modular The entire purpose of PyTorch is the ability to code Pythonic machine learning-related code.

There are two primary methods for writing machine-learning code using Python:

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

In the PyTorch Go Modular portion of this class, students will be taught how to use your most valuable Jupyter/GoogleColab Notebook code and transform it into reusable Python scripts. This is the way you'll come across PyTorch code with the world.

7. PyTorch Transfer Learning What if you could take what one model's experience has taught and use it to solve your issues? That's precisely what PyTorch Transfer Learning covers.

Learn about the benefits of transfer learning and how it allows the user to select a machine-learning model that has been trained using hundreds of thousands of pictures, alter it a bit, and improve the efficiency that comes with FoodVision Mini, saving you time and money.

8. PyTorch Experiment Tracking We'll begin cooking using heat by beginning Phase 1 in Our Milestone Project of the course!

By now, you've constructed several PyTorch models. However, how do you know which one is the most effective?

This is where the PyTorch Experiment tracking comes in.

In line with the mantra of the machine learning practitioner: experiment and test! You'll set up a system to track different FoodVision Mini experiment results and evaluate them to determine the most successful.

9. PyTorch Paper Replicating -Machine learning is increasing. Research papers are published each day. To be able to read and comprehend these research papers requires some time and endless practice.

This is the subject PyTorch Paper Replicating covers. You'll be taught how to read through a machine-learning research paper and then replicate the paper using PyTorch code.

In this stage, you'll take on the second part 2 of the Milestone Project, where you'll recreate the revolutionary Vision Transformer architecture!

10. PyTorch Model Deployment By now, your FoodVision model is performing exceptionally well. However, till this point, you've been the only one who could access it.

How can to get PyTorch models into the hands of other people?

This is what PyTorch Model Deployment covers. In the third part of the Milestone Project, you'll learn how to use the top performer FoodVision Mini model and deploy it on the internet for other users to access and test it using their food pictures.

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