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
Show more
Show less

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

Get Your Hands Dirty, Practical Coding Experience, Data Links

How to use Github & Extra Coding Tips (Optional)

Where to get the code, notebooks, and data

How to Succeed in This Course

Temporary 403 Errors

Google Colab

Intro to Google Colab, how to use a GPU or TPU for free

Uploading your own data to Google Colab

Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Machine Learning and Neurons

What is Machine Learning?

Regression Basics

Regression Code Preparation

Regression Notebook

Moore's Law

Moore's Law Notebook

Linear Classification Basics

Classification Code Preparation

Classification Notebook

Saving and Loading a Model

A Short Neuroscience Primer

How does a model "learn"?

Model With Logits

Train Sets vs. Validation Sets vs. Test Sets

Suggestion Box

Feedforward Artificial Neural Networks

Artificial Neural Networks Section Introduction

Forward Propagation

The Geometrical Picture

Activation Functions

Multiclass Classification

How to Represent Images

Color Mixing Clarification

Code Preparation (ANN)

ANN for Image Classification

ANN for Regression

How to Choose Hyperparameters

Convolutional Neural Networks

What is Convolution? (part 1)

What is Convolution? (part 2)

What is Convolution? (part 3)

Convolution on Color Images

CNN Architecture

CNN Code Preparation (part 1)

CNN Code Preparation (part 2)

CNN Code Preparation (part 3)

CNN for Fashion MNIST

CNN for CIFAR-10

Data Augmentation

Batch Normalization

Improving CIFAR-10 Results

Recurrent Neural Networks, Time Series, and Sequence Data

Sequence Data

Forecasting

Autoregressive Linear Model for Time Series Prediction

Proof that the Linear Model Works

Recurrent Neural Networks

RNN Code Preparation

RNN for Time Series Prediction

Paying Attention to Shapes

GRU and LSTM (pt 1)

GRU and LSTM (pt 2)

A More Challenging Sequence

RNN for Image Classification (Theory)

RNN for Image Classification (Code)

Stock Return Predictions using LSTMs (pt 1)

Stock Return Predictions using LSTMs (pt 2)

Stock Return Predictions using LSTMs (pt 3)

Other Ways to Forecast

Natural Language Processing (NLP)

Embeddings

Neural Networks with Embeddings

Text Preprocessing Concepts

Beginner Blues - PyTorch NLP Version

(Legacy) Text Preprocessing Code Preparation

(Legacy) Text Preprocessing Code Example

Text Classification with LSTMs (V2)

CNNs for Text

Text Classification with CNNs (V2)

(Legacy) VIP: Making Predictions with a Trained NLP Model

VIP: Making Predictions with a Trained NLP Model (V2)

Recommender Systems

Recommender Systems with Deep Learning Theory

Recommender Systems with Deep Learning Code Preparation

Recommender Systems with Deep Learning Code (pt 1)

Recommender Systems with Deep Learning Code (pt 2)

VIP: Making Predictions with a Trained Recommender Model

Transfer Learning for Computer Vision

Transfer Learning Theory

Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)

Large Datasets

2 Approaches to Transfer Learning

Transfer Learning Code (pt 1)

Transfer Learning Code (pt 2)

GANs (Generative Adversarial Networks)

GAN Theory

Good to know

Know what's good

, what to watch for

, 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

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

Show steps

Brush up on your machine learning skills before the course begins

Browse courses on Machine Learning

Show steps

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

Practice Python Coding

Show steps

Improve your Python coding skills before the course begins

Browse courses on Python

Show steps

Solve some Python coding problems
Build a small Python project

Read Deep Learning with Python by François Chollet

Show steps

Review the essential concepts of deep learning before the course starts

View Deep Learning with Python, Second Edition on Amazon

Show steps

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

Show steps

Get introduced to the PyTorch library through tutorial videos

Browse courses on PyTorch

Show steps

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

Attend a Machine Learning Meetup

Show steps

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

Browse courses on Machine Learning

Show steps

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

Build a Simple Image Classifier

Show steps

Apply the concepts learned in the course to a practical project

Browse courses on Image Classification

Show steps

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

Attend a PyTorch Workshop

Show steps

Deepen your understanding of PyTorch through hands-on practice

Browse courses on PyTorch

Show steps

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

Write a Blog Post

Show steps

Summarize and share your learnings from the course

Browse courses on Deep Learning

Show steps

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:

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

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

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

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

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

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.

See salaries and explore the career path for Financial Analyst

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