PyTorch for Deep Learning Bootcamp: Zero to Mastery

What is PyTorch and why is it valuable?

PyTorch is a robust machine learning and deep learning framework, developed in Python.

With PyTorch, you can create innovative deep learning algorithms and utilize advanced neural networks that power many current Artificial Intelligence (AI) applications.

It’s also hugely popular these days, which means there are numerous job opportunities!

Notable companies using PyTorch include:

• Tesla, which uses it for the computer vision technology in their self-driving vehicles
• Meta, leveraging it for curation and content understanding on their platforms
• Apple, employing it for superior photography capabilities.

What’s more exciting?

A significant portion of leading machine learning research is based on PyTorch, so mastering it places you at the forefront of this fast-evolving field.

You'll learn PyTorch along with a supportive community.

Alumni from Zero To Mastery have secured roles at top tech companies like Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify, and many more in machine learning and deep learning.

You can be the next success story.

By enrolling today, you’ll join our exclusive live online community classroom, interacting with thousands of students, mentors, TAs, and instructors.

The best part? You’ll learn PyTorch from an experienced machine learning engineer who is also an outstanding instructor!

What does this PyTorch course include?

This course is very practical and project-focused. No more mindless screen-staring; save that for other tutorials and classes.

• Conduct experiments
• Engage in exercises to enhance your skills
• Create real deep learning models and projects inspired by real-life scenarios

By the conclusion, you’ll have a solid skill set to address and create solutions for modern deep learning challenges encountered by major tech firms.

⚠ Heads up: This course is comprehensive. However, rest assured—Daniel will guide you every step of the way, starting from the basics!

What you'll learn in this PyTorch course:

1. PyTorch Foundations — We start with key basics, ensuring even beginners can follow along.

In machine learning, data is represented as tensors (collections of numbers). Mastering tensor creation in PyTorch is crucial for developing machine learning algorithms. This section prominently covers the PyTorch tensor datatype.

2. PyTorch Process — Once you know the basics and have created tensors, what comes next?

With the PyTorch Process, you’ll learn how to move from data to tensors to trained neural network models. These steps will become second nature during your PyTorch journey.

3. Neural Network Classification in PyTorch — Classification is a core machine learning challenge.

• How do you classify an object?
• Is an email spam?
• Is a credit card transaction fraudulent?

In this section, you’ll learn to build a neural network classification model using PyTorch to answer these questions.

4. PyTorch for Computer Vision — Neural networks have transformed computer vision, and PyTorch is central to many advances in this area.

Tesla, for example, utilizes PyTorch for their self-driving algorithms.

In the PyTorch for Computer Vision section, you'll develop a PyTorch neural network that identifies patterns in images and categorizes them accordingly.

5. Creating Custom Datasets in PyTorch — The true magic of machine learning lies in uncovering patterns within your unique datasets. Though many pre-existing datasets are available, how do you integrate your own into PyTorch?

This section will teach you how to load a custom image dataset for FoodVision Mini, a computer vision model capable of classifying images of dishes like pizza, steak, and sushi (feeling hungry to learn yet?).

We’ll revisit FoodVision Mini throughout the course.

6. Organizing Code in PyTorch — PyTorch is tailored for writing Pythonic machine learning code.

There are two main methods for writing machine learning code in Python:

1. Using a Jupyter/Google Colab notebook (ideal for experimentation)
2. Developing Python scripts (great for reproducibility and modularity)

In this section, you’ll learn to convert your most valuable Jupyter/Google Colab code into reusable Python scripts, which is standard practice in the field.

7. Utilizing Transfer Learning in PyTorch — What if you could apply learnings from one model to your own problems? That's what the Transfer Learning section teaches.

You'll learn how to use this technique to adapt a pre-trained model (trained on millions of images) to improve clarity and performance in your FoodVision Mini project, saving you valuable time and resources.

8. Monitoring Experiments in PyTorch — By this stage, you'll begin Part 1 of our Milestone Project!

Having built several PyTorch models, you might wonder how to keep track of which performs best.

This is where monitoring experiments comes into play.

Adhering to the motto of experiment, experiment, experiment!, you'll establish a system to log various outcomes from the FoodVision Mini experiments and analyze them to pinpoint the best results.

9. Replicating Research Papers in PyTorch — With the rapid growth of machine learning, new research papers are published daily. Understanding them takes time and practice.

This section trains you to dissect and replicate a machine learning research paper using PyTorch.

You’ll also move to Part 2 of our Milestone Project, where you'll replicate the innovative Vision Transformer architecture!

10. Deploying Models in PyTorch — By now, your FoodVision model should be performing exceptionally well. However, so far, only you have access to it.

So, how can you share your PyTorch models with others?

This section covers model deployment. In Part 3 of your Milestone Project, you’ll learn to deploy your top-performing FoodVision Mini model to the web, allowing others to upload and test it with their own food images.

What’s the takeaway?

The demand for machine learning is skyrocketing, with deep learning elevating your expertise to a higher level. More job opportunities are seeking this specialized knowledge.

Companies such as Tesla, Microsoft, OpenAI, Meta (formerly Facebook + Instagram), Airbnb, and many more rely on PyTorch.

This is the most comprehensive online bootcamp available to master PyTorch and kickstart your career as a Deep Learning Engineer.

So why wait? Enhance your career and boost your earning potential by mastering PyTorch and adding deep learning to your skillset 💪.

You have nothing to lose. Start learning now, and if this course doesn't meet your expectations, we offer a 100% refund within 30 days. No hassle, no questions asked.