PyTorch for Deep Learning Bootcamp

PyTorch for Deep Learning Bootcamp, available at $99.99, has an average rating of 4.61, with 358 lectures, based on 3539 reviews, and has 26287 subscribers.

You will learn about Everything from getting started with using PyTorch to building your own real-world models Understand how to integrate Deep Learning into tools and applications Build and deploy your own custom trained PyTorch neural network accessible to the public Master deep learning and become a top candidate for recruiters seeking Deep Learning Engineers The skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year Why PyTorch is a fantastic way to start working in machine learning Create and utilize machine learning algorithms just like you would write a Python program How to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applications To expand your Machine Learning and Deep Learning skills and toolkit This course is ideal for individuals who are Anyone who wants a step-by-step guide to learning PyTorch and be able to get hired as a Deep Learning Engineer making over $100,000 / year or Students, developers, and data scientists who want to demonstrate practical machine learning skills by actually building and training real models using PyTorch or Anyone looking to expand their knowledge and toolkit when it comes to AI, Machine Learning and Deep Learning or Bootcamp or online PyTorch tutorial graduates that want to go beyond the basics or Students who are frustrated with their current progress with all of the beginner PyTorch tutorials out there that don't go beyond the basics and don't give you real-world practice or skills you need to actually get hired It is particularly useful for Anyone who wants a step-by-step guide to learning PyTorch and be able to get hired as a Deep Learning Engineer making over $100,000 / year or Students, developers, and data scientists who want to demonstrate practical machine learning skills by actually building and training real models using PyTorch or Anyone looking to expand their knowledge and toolkit when it comes to AI, Machine Learning and Deep Learning or Bootcamp or online PyTorch tutorial graduates that want to go beyond the basics or Students who are frustrated with their current progress with all of the beginner PyTorch tutorials out there that don't go beyond the basics and don't give you real-world practice or skills you need to actually get hired.

Enroll now: PyTorch for Deep Learning Bootcamp

Summary

Title: PyTorch for Deep Learning Bootcamp

Price: $99.99

Average Rating: 4.61

Number of Lectures: 358

Number of Published Lectures: 358

Number of Curriculum Items: 358

Number of Published Curriculum Objects: 358

Original Price: $174.99

Quality Status: approved

Status: Live

What You Will Learn

Everything from getting started with using PyTorch to building your own real-world models
Understand how to integrate Deep Learning into tools and applications
Build and deploy your own custom trained PyTorch neural network accessible to the public
Master deep learning and become a top candidate for recruiters seeking Deep Learning Engineers
The skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year
Why PyTorch is a fantastic way to start working in machine learning
Create and utilize machine learning algorithms just like you would write a Python program
How to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applications
To expand your Machine Learning and Deep Learning skills and toolkit

Who Should Attend

Anyone who wants a step-by-step guide to learning PyTorch and be able to get hired as a Deep Learning Engineer making over $100,000 / year
Students, developers, and data scientists who want to demonstrate practical machine learning skills by actually building and training real models using PyTorch
Anyone looking to expand their knowledge and toolkit when it comes to AI, Machine Learning and Deep Learning
Bootcamp or online PyTorch tutorial graduates that want to go beyond the basics
Students who are frustrated with their current progress with all of the beginner PyTorch tutorials out there that don't go beyond the basics and don't give you real-world practice or skills you need to actually get hired

Target Audiences

Anyone who wants a step-by-step guide to learning PyTorch and be able to get hired as a Deep Learning Engineer making over $100,000 / year
Students, developers, and data scientists who want to demonstrate practical machine learning skills by actually building and training real models using PyTorch
Anyone looking to expand their knowledge and toolkit when it comes to AI, Machine Learning and Deep Learning
Bootcamp or online PyTorch tutorial graduates that want to go beyond the basics
Students who are frustrated with their current progress with all of the beginner PyTorch tutorials out there that don't go beyond the basics and don't give you real-world practice or skills you need to actually get hired

What is PyTorch and why should I learn it?

PyTorch is a machine learning and deep learning framework written in Python.

PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks powering much of today’s Artificial Intelligence (AI) applications.

Plus it’s so hot right now, so there’s lots of jobs available!

PyTorch is used by companies like:

Tesla to build the computer vision systems for their self-driving cars
Meta to power the curation and understanding systems for their content timelines
Apple to create computationally enhanced photography.

Want to know what’s even cooler?

Much of the latest machine learning research is done and published using PyTorch code so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.

And you’ll be learning PyTorch in good company.

Graduates of Zero To Mastery are now working at Google, Tesla, Amazon, Apple, IBM, Uber, Meta, Shopify + other top tech companies at the forefront of machine learning and deep learning.

This can be you.

By enrolling today, you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you will be learning PyTorch from a professional machine learning engineer, with real-world experience, and who is one of the best teachers around!

What will this PyTorch course be like?

This PyTorch course is very hands-on and project based. You won’t just be staring at your screen. We’ll leave that for other PyTorch tutorials and courses.

In this course you’ll actually be:

Running experiments
Completing exercises to test your skills
Building real-world deep learning models and projects to mimic real life scenarios

By the end of it all, you’ll have the skillset needed to identify and develop modern deep learning solutions that Big Tech companies encounter.

Fair warning: this course is very comprehensive. But don’t be intimidated, Daniel will teach you everything from scratch and step-by-step!

Here’s what you’ll learn in this PyTorch course:

1. PyTorch Fundamentals — We start with the barebone fundamentals, so even if you’re a beginner you’ll get up to speed.

In machine learning, data gets represented as a tensor (a collection of numbers). Learning how to craft tensors with PyTorch is paramount to building machine learning algorithms. In PyTorch Fundamentals we cover the PyTorch tensor datatype in-depth.

2. PyTorch Workflow — Okay, you’ve got the fundamentals down, and you’ve made some tensors to represent data, but what now?

With PyTorch Workflow you’ll learn the steps to go from data -> tensors -> trained neural network model. You’ll see and use these steps wherever you encounter PyTorch code as well as for the rest of the course.

3. PyTorch Neural Network Classification — Classification is one of the most common machine learning problems.

Is something one thing or another?
Is an email spam or not spam?
Is credit card transaction fraud or not fraud?

With PyTorch Neural Network Classification you’ll learn how to code a neural network classification model using PyTorch so that you can classify things and answer these questions.

4. PyTorch Computer Vision — Neural networks have changed the game of computer vision forever. And now PyTorch drives many of the latest advancements in computer vision algorithms.

For example, Tesla use PyTorch to build the computer vision algorithms for their self-driving software.

With PyTorch Computer Vision you’ll build a PyTorch neural network capable of seeing patterns in images of and classifying them into different categories.

5. PyTorch Custom Datasets — The magic of machine learning is building algorithms to find patterns in your own custom data. There are plenty of existing datasets out there, but how do you load your own custom dataset into PyTorch?

This is exactly what you’ll learn with the PyTorch Custom Datasets section of this course.

You’ll learn how to load an image dataset for FoodVision Mini: a PyTorch computer vision model capable of classifying images of pizza, steak and sushi (am I making you hungry to learn yet?!).

We’ll be building upon FoodVision Mini for the rest of the course.

6. PyTorch Going Modular — The whole point of PyTorch is to be able to write Pythonic machine learning code.

There are two main tools for writing machine learning code with Python:

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

In the PyTorch Going Modular section of this course, you’ll learn how to take your most useful Jupyter/Google Colab Notebook code and turn it reusable Python scripts. This is often how you’ll find PyTorch code shared in the wild.

7. PyTorch Transfer Learning — What if you could take what one model has learned and leverage it for your own problems? That’s what PyTorch Transfer Learning covers.

You’ll learn about the power of transfer learning and how it enables you to take a machine learning model trained on millions of images, modify it slightly, and enhance the performance of FoodVision Mini, saving you time and resources.

8. PyTorch Experiment Tracking — Now we’re going to start cooking with heat by starting Part 1 of our Milestone Project of the course!

At this point you’ll have built plenty of PyTorch models. But how do you keep track of which model performs the best?

That’s where PyTorch Experiment Tracking comes in.

Following the machine learning practitioner’s motto of experiment, experiment, experiment! you’ll setup a system to keep track of various FoodVision Mini experiment results and then compare them to find the best.

9. PyTorch Paper Replicating — The field of machine learning advances quickly. New research papers get published every day. Being able to read and understand these papers takes time and practice.

So that’s what PyTorch Paper Replicating covers. You’ll learn how to go through a machine learning research paper and replicate it with PyTorch code.

At this point you’ll also undertake Part 2 of our Milestone Project, where you’ll replicate the groundbreaking Vision Transformer architecture!

10. PyTorch Model Deployment — By this stage your FoodVision model will be performing quite well. But up until now, you’ve been the only one with access to it.

How do you get your PyTorch models in the hands of others?

That’s what PyTorch Model Deployment covers. In Part 3 of your Milestone Project, you’ll learn how to take the best performing FoodVision Mini model and deploy it to the web so other people can access it and try it out with their own food images.

What’s the bottom line?

Machine learning’s growth and adoption is exploding, and deep learning is how you take your machine learning knowledge to the next level. More and more job openings are looking for this specialized knowledge.

Companies like Tesla, Microsoft, OpenAI, Meta (Facebook + Instagram), Airbnb and many others are currently powered by PyTorch.

And this is the most comprehensive online bootcamp to learn PyTorch and kickstart your career as a Deep Learning Engineer.

So why wait? Advance your career and earn a higher salary by mastering PyTorch and adding deep learning to your toolkit?

Course Curriculum

Chapter 1: Introduction

Lecture 1: PyTorch for Deep Learning

Lecture 2: Course Welcome and What Is Deep Learning

Lecture 3: Join Our Online Classroom!

Lecture 4: Exercise: Meet Your Classmates + Instructor

Lecture 5: Free Course Book + Code Resources + Asking Questions + Getting Help

Lecture 6: ZTM Resources

Lecture 7: Machine Learning + Python Monthly Newsletters

Chapter 2: PyTorch Fundamentals

Lecture 1: Why Use Machine Learning or Deep Learning

Lecture 2: The Number 1 Rule of Machine Learning and What Is Deep Learning Good For

Lecture 3: Machine Learning vs. Deep Learning

Lecture 4: Anatomy of Neural Networks

Lecture 5: Different Types of Learning Paradigms

Lecture 6: What Can Deep Learning Be Used For

Lecture 7: What Is and Why PyTorch

Lecture 8: What Are Tensors

Lecture 9: What We Are Going To Cover With PyTorch

Lecture 10: How To and How Not To Approach This Course

Lecture 11: Important Resources For This Course

Lecture 12: Getting Setup to Write PyTorch Code

Lecture 13: Introduction to PyTorch Tensors

Lecture 14: Creating Random Tensors in PyTorch

Lecture 15: Creating Tensors With Zeros and Ones in PyTorch

Lecture 16: Creating a Tensor Range and Tensors Like Other Tensors

Lecture 17: Dealing With Tensor Data Types

Lecture 18: Getting Tensor Attributes

Lecture 19: Manipulating Tensors (Tensor Operations)

Lecture 20: Matrix Multiplication (Part 1)

Lecture 21: Matrix Multiplication (Part 2): The Two Main Rules of Matrix Multiplication

Lecture 22: Matrix Multiplication (Part 3): Dealing With Tensor Shape Errors

Lecture 23: Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation)

Lecture 24: Finding The Positional Min and Max of Tensors

Lecture 25: Reshaping, Viewing and Stacking Tensors

Lecture 26: Squeezing, Unsqueezing and Permuting Tensors

Lecture 27: Selecting Data From Tensors (Indexing)

Lecture 28: PyTorch Tensors and NumPy

Lecture 29: PyTorch Reproducibility (Taking the Random Out of Random)

Lecture 30: Different Ways of Accessing a GPU in PyTorch

Lecture 31: Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU

Lecture 32: PyTorch Fundamentals: Exercises and Extra-Curriculum

Chapter 3: PyTorch Workflow

Lecture 1: Introduction and Where You Can Get Help

Lecture 2: Getting Setup and What We Are Covering

Lecture 3: Creating a Simple Dataset Using the Linear Regression Formula

Lecture 4: Splitting Our Data Into Training and Test Sets

Lecture 5: Building a function to Visualize Our Data

Lecture 6: Creating Our First PyTorch Model for Linear Regression

Lecture 7: Breaking Down What's Happening in Our PyTorch Linear regression Model

Lecture 8: Discussing Some of the Most Important PyTorch Model Building Classes

Lecture 9: Checking Out the Internals of Our PyTorch Model

Lecture 10: Making Predictions With Our Random Model Using Inference Mode

Lecture 11: Training a Model Intuition (The Things We Need)

Lecture 12: Setting Up an Optimizer and a Loss Function

Lecture 13: PyTorch Training Loop Steps and Intuition

Lecture 14: Writing Code for a PyTorch Training Loop

Lecture 15: Reviewing the Steps in a Training Loop Step by Step

Lecture 16: Running Our Training Loop Epoch by Epoch and Seeing What Happens

Lecture 17: Writing Testing Loop Code and Discussing What's Happening Step by Step

Lecture 18: Reviewing What Happens in a Testing Loop Step by Step

Lecture 19: Writing Code to Save a PyTorch Model

Lecture 20: Writing Code to Load a PyTorch Model

Lecture 21: Setting Up to Practice Everything We Have Done Using Device Agnostic code

Lecture 22: Putting Everything Together (Part 1): Data

Lecture 23: Putting Everything Together (Part 2): Building a Model

Lecture 24: Putting Everything Together (Part 3): Training a Model

Lecture 25: Putting Everything Together (Part 4): Making Predictions With a Trained Model

Lecture 26: Putting Everything Together (Part 5): Saving and Loading a Trained Model

Lecture 27: Exercise: Imposter Syndrome

Lecture 28: PyTorch Workflow: Exercises and Extra-Curriculum

Chapter 4: PyTorch Neural Network Classification

Lecture 1: Introduction to Machine Learning Classification With PyTorch

Lecture 2: Classification Problem Example: Input and Output Shapes

Lecture 3: Typical Architecture of a Classification Neural Network (Overview)

Lecture 4: Making a Toy Classification Dataset

Lecture 5: Turning Our Data into Tensors and Making a Training and Test Split

Lecture 6: Laying Out Steps for Modelling and Setting Up Device-Agnostic Code

Lecture 7: Coding a Small Neural Network to Handle Our Classification Data

Lecture 8: Making Our Neural Network Visual

Lecture 9: Recreating and Exploring the Insides of Our Model Using nn.Sequential

Lecture 10: Loss Function Optimizer and Evaluation Function for Our Classification Network

Lecture 11: Going from Model Logits to Prediction Probabilities to Prediction Labels

Lecture 12: Coding a Training and Testing Optimization Loop for Our Classification Model

Lecture 13: Writing Code to Download a Helper Function to Visualize Our Models Predictions

Lecture 14: Discussing Options to Improve a Model

Lecture 15: Creating a New Model with More Layers and Hidden Units

Lecture 16: Writing Training and Testing Code to See if Our Upgraded Model Performs Better

Lecture 17: Creating a Straight Line Dataset to See if Our Model is Learning Anything

Lecture 18: Building and Training a Model to Fit on Straight Line Data

Lecture 19: Evaluating Our Models Predictions on Straight Line Data

Lecture 20: Introducing the Missing Piece for Our Classification Model Non-Linearity

Lecture 21: Building Our First Neural Network with Non-Linearity

Lecture 22: Writing Training and Testing Code for Our First Non-Linear Model

Lecture 23: Making Predictions with and Evaluating Our First Non-Linear Model

Lecture 24: Replicating Non-Linear Activation Functions with Pure PyTorch

Lecture 25: Putting It All Together (Part 1): Building a Multiclass Dataset

Lecture 26: Creating a Multi-Class Classification Model with PyTorch

Lecture 27: Setting Up a Loss Function and Optimizer for Our Multi-Class Model

Lecture 28: Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model

Lecture 29: Training a Multi-Class Classification Model and Troubleshooting Code on the Fly

Instructors

Andrei Neagoie
Founder of zerotomastery.io
Daniel Bourke
Machine Learning Engineer/Writer/Video maker

Rating Distribution

1 stars: 37 votes
2 stars: 49 votes
3 stars: 177 votes
4 stars: 906 votes
5 stars: 2373 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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