PyTorch: Deep Learning and Artificial Intelligence from Udemy

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

Welcome to PyTorch: Deep Learning and Artificial Intelligence.

Although Google's Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.

Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?

Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?

It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)

On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.

If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.

Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photo-realistic images of people and things that never existed (GANs)
Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
Self-driving cars (Computer Vision)
Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

Natural Language Processing (NLP)
Recommender Systems
Transfer Learning for Computer Vision
Generative Adversarial Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses PyTorch, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

I'm taking the approach that even if you are not 100% comfortable with the mathematical concepts, you can still do this. In this course, we focus more on the PyTorch library, rather than deriving any mathematical equations. I have tons of courses for that already, so there is no need to repeat that here.

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out

What's inside

Learning objectives

Artificial neural networks (anns) / deep neural networks (dnns)
Predict stock returns
Time series forecasting
Computer vision
How to build a deep reinforcement learning stock trading bot
Gans (generative adversarial networks)
Recommender systems
Image recognition

Convolutional neural networks (cnns)
Recurrent neural networks (rnns)
Natural language processing (nlp) with deep learning
Demonstrate moore's law using code
Transfer learning to create state-of-the-art image classifiers
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
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Artificial neural networks (anns) / deep neural networks (dnns)
Predict stock returns
Time series forecasting
Computer vision
How to build a deep reinforcement learning stock trading bot
Gans (generative adversarial networks)
Recommender systems
Image recognition
Convolutional neural networks (cnns)
Recurrent neural networks (rnns)
Natural language processing (nlp) with deep learning
Demonstrate moore's law using code
Transfer learning to create state-of-the-art image classifiers
Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
Show more
Show less

Syllabus

Introduction

Welcome

Overview and Outline

Getting Set Up

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers PyTorch, which is used by top AI firms like OpenAI, Apple, and JPMorgan Chase

Teaches foundations of groundbreaking applications like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Emphasizes building and testing new ideas quickly and easily

Offers hands-on projects to apply knowledge directly

Provides multiple levels of learning, catering to beginners to experts

Emphasizes breadth rather than depth, suitable for learners seeking a wide understanding

Reviews summary

Practical pytorch for applied deep learning

Students say this course provides a highly practical and comprehensive journey into Deep Learning with PyTorch. Learners consistently commend the instructor's clear explanations and the course's hands-on, project-driven approach, which explores a broad spectrum of AI topics from CNNs to GANs. It's ideally suited for career-focused individuals and professionals with a Python background aiming for immediate application. While prioritizing breadth over in-depth theory, this design is beneficial for practical skill development. A significant positive trend is the continuous content updates, including PyTorch 2.0, ensuring its relevance. Some without a strong ML/Numpy prerequisite may find the pace demanding.

Covers a wide range of deep learning architectures and applications.

"I was impressed by the sheer breadth of topics covered, from CNNs and RNNs to GANs and NLP."

"This course provided me with a solid overview of various deep learning domains, which is exactly what I needed."

"It covers almost all major deep learning architectures with practical code, which is invaluable."

Instructor provides clear, concise, and engaging explanations.

"The instructor's explanations are crystal clear and easy to follow, even for complex topics."

"I found the instructor to be highly knowledgeable and capable of explaining concepts in a very digestible way."

"His passion for the subject really comes through, making the lectures engaging and insightful."

The course content is actively maintained and updated, reflecting new technologies.

"I appreciate that the instructor keeps updating the course, especially the PyTorch 2.0 sections and new projects."

"It's great to see new content, like the stock prediction project, being added and older parts being refreshed."

"The continuous improvements make this course highly relevant and a worthwhile investment."

Emphasizes hands-on coding and real-world applications.

"This course is absolutely fantastic for hands-on learning. I particularly enjoyed building practical applications."

"I appreciate the code-heavy approach; it solidified my understanding of PyTorch concepts through practical examples."

"The projects were the most valuable part for me; I could immediately apply what I learned to my own work."

"It's a great course if you want to learn by doing, not just by listening to theory."

Focuses on practical implementation rather than deep mathematical derivations.

"I wish there was more mathematical theory and derivation behind the models; it felt a bit light on that aspect."

"This course is excellent for coding, but if you're looking for deep theoretical dives, you might need supplements."

"I found the course focused more on 'how to use' PyTorch rather than 'why it works' mathematically, which suited my needs but might not suit others."

Ideal for those with Python/NumPy, may be fast for absolute beginners.

"I think it's a great course if you have some Python and ML background, but it moves quickly for absolute beginners."

"It’s definitely not for complete beginners to programming or machine learning; I struggled without prior knowledge."

"If you've done the instructor's Numpy prerequisite, you'll be fine. Otherwise, it might feel a bit fast."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in PyTorch: Deep Learning and Artificial Intelligence with these activities:

Learn the Basics of Machine Learning

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Brush up on your machine learning skills before the course begins

Browse courses on Machine Learning

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Take a free online course on machine learning
Read a book on machine learning
Solve some machine learning problems

Practice Python Coding

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Improve your Python coding skills before the course begins

Browse courses on Python

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Solve some Python coding problems
Build a small Python project

Read Deep Learning with Python by François Chollet

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Review the essential concepts of deep learning before the course starts

View Deep Learning with Python, Second Edition on Amazon

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Go to Amazon.com and purchase the book
Read the book's introduction and first few chapters
Solve the exercises in the book

Five other activities

Expand to see all activities and additional details

Show all eight activities

Watch PyTorch Tutorials

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Get introduced to the PyTorch library through tutorial videos

Browse courses on PyTorch

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Watch some PyTorch tutorials on YouTube
Follow along with the tutorials and try to code along

Attend a Machine Learning Meetup

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Connect with other people who are interested in machine learning and deep learning

Browse courses on Machine Learning

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Find a machine learning meetup in your area
Attend the meetup
Network with other attendees

Build a Simple Image Classifier

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Apply the concepts learned in the course to a practical project

Browse courses on Image Classification

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Gather a dataset of images
Build a simple image classifier using PyTorch
Evaluate the performance of your classifier

Attend a PyTorch Workshop

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Deepen your understanding of PyTorch through hands-on practice

Browse courses on PyTorch

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Find a PyTorch workshop in your area
Attend the workshop
Follow along with the workshop exercises

Write a Blog Post

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Summarize and share your learnings from the course

Browse courses on Deep Learning

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Choose a topic related to deep learning or PyTorch
Write a blog post on the topic
Publish your blog post

Career center

Learners who complete PyTorch: Deep Learning and Artificial Intelligence will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design, develop, and implement machine learning models. The PyTorch library is particularly well-suited to professionals in this field and this course can help to advance the skillset of a Machine Learning Engineer significantly.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists work at the intersection of programming, statistics, and mathematics to extract meaningful insights from data. Proficiency with the PyTorch library is an extremely useful skill in this role; this course offers instruction in PyTorch, making it a highly relevant choice for a Data Scientist.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers work to apply concepts of machine learning and AI to solve real-world problems. This course provides instruction in the PyTorch library which is employed by leaders in the AI industry and may prove a valuable tool in the toolbelt of an AI Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers work on the processing of human language data. PyTorch frequently is used in this role and thus this course may be of assistance to a Natural Language Processing Engineer.

See salaries and explore the career path for Natural Language Processing Engineer

Computer Vision Engineer

Computer Vision Engineers develop and maintain computer vision models, and PyTorch is often employed in this capacity. This course in PyTorch can provide valuable new skills that may be useful to a Computer Vision Engineer.

See salaries and explore the career path for Computer Vision Engineer

Software Engineer

Software Engineers build and maintain computer programs, and PyTorch often used in this capacity. This course can assist a Software Engineer in acquiring a valuable new skill to add to their repertoire.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts are involved in the analysis of data in order to uncover trends and patterns. With the increasing use of machine learning and artificial intelligence, PyTorch has become an increasingly useful tool for Data Analysts, making this course well-suited for someone looking for a boost in their career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts play a vital role in the finance industry. They use mathematical and statistical methods to evaluate risk and make investment decisions. Knowledge of PyTorch may prove valuable to a Quant seeking to expand their toolkit and this course provides a solid foundation in PyTorch for exactly this purpose.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use their knowledge of mathematics, statistics, and risk to assess and mitigate financial risks. PyTorch may be employed in this role and thus this course may be of benefit to an Actuary by broadening their skillset.

See salaries and explore the career path for Actuary

Financial Analyst

Financial Analysts provide insights and recommendations to businesses and investors. PyTorch may be employed to enhance the accuracy of their analysis and this course can play a role providing instruction in its use.

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Risk Manager

Risk Managers develop and implement strategies to manage risks faced by companies and organizations. By adding PyTorch to their skillset via this course they may be able to access new and innovative approaches to risk management.

See salaries and explore the career path for Risk Manager

Statistician

Statisticians collect, analyze, interpret, and present data. This course can augment the capabilities of a Statistician by providing instruction in the use of PyTorch in this field.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use advanced analytical techniques to solve complex business problems. PyTorch may be utilized in the problem-solving methods employed by these analysts.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts assess an organization's operations and formulate recommendations for improvements. This course may help a Business Analyst by familiarizing them with the use of PyTorch in business analysis.

See salaries and explore the career path for Business Analyst

Management Consultant

Management Consultants help organizations improve their performance. PyTorch is a tool that may aid in this objective, and taking this course can give a Management Consultant the skills necessary to do just that.

See salaries and explore the career path for Management Consultant