PyTorch: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

4.88 (1878 reviews)
Udemy
platform
English
language
Data Science
category
9,285
students
24.5 hours
content
Mar 2024
last update
$74.99
regular price

What you will learn

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

Description

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

Content

Introduction

Welcome
Overview and Outline
Where to get the Code

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

Feedforward Artificial Neural Networks

Artificial Neural Networks Section Introduction
Forward Propagation
The Geometrical Picture
Activation Functions
Multiclass Classification
How to Represent Images
Code Preparation (ANN)
ANN for Image Classification
ANN for Regression

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)

Natural Language Processing (NLP)

Embeddings
Neural Networks with Embeddings
Text Preprocessing (pt 1)
Text Preprocessing (pt 2)
Text Preprocessing (pt 3)
Text Classification with LSTMs
CNNs for Text
Text Classification with CNNs

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)

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
GAN Code Preparation
GAN Code

Deep Reinforcement Learning (Theory)

Deep Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to “learn”?
Solving the Bellman Equation with Reinforcement Learning (pt 1)
Solving the Bellman Equation with Reinforcement Learning (pt 2)
Epsilon-Greedy
Q-Learning
Deep Q-Learning / DQN (pt 1)
Deep Q-Learning / DQN (pt 2)
How to Learn Reinforcement Learning

Stock Trading Project with Deep Reinforcement Learning

Reinforcement Learning Stock Trader Introduction
Data and Environment
Replay Buffer
Program Design and Layout
Code pt 1
Code pt 2
Code pt 3
Code pt 4
Reinforcement Learning Stock Trader Discussion

VIP: Uncertainty Estimation

Custom Loss and Estimating Prediction Uncertainty
Estimating Prediction Uncertainty Code

VIP: Facial Recognition

Facial Recognition Section Introduction
Siamese Networks
Code Outline
Loading in the data
Splitting the data into train and test
Converting the data into pairs
Generating Generators
Creating the model and loss
Accuracy and imbalanced classes
Facial Recognition Section Summary

In-Depth: Loss Functions

Mean Squared Error
Binary Cross Entropy
Categorical Cross Entropy

In-Depth: Gradient Descent

Gradient Descent
Stochastic Gradient Descent
Momentum
Variable and Adaptive Learning Rates
Adam

Extras

Links To Colab Notebooks
Links to VIP Notebooks

Setting up your Environment

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Windows-Focused Environment Setup 2018
Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer

Appendix / FAQ

What is the Appendix?
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
How to Code Yourself (part 1)
How to Code Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
How to Succeed in this Course (Long Version)
What order should I take your courses in? (part 1)
What order should I take your courses in? (part 2)
BONUS: Where to get discount coupons and FREE deep learning material

Screenshots

PyTorch: Deep Learning and Artificial Intelligence - Screenshot_01PyTorch: Deep Learning and Artificial Intelligence - Screenshot_02PyTorch: Deep Learning and Artificial Intelligence - Screenshot_03PyTorch: Deep Learning and Artificial Intelligence - Screenshot_04

Reviews

David
November 4, 2023
Great course for getting the essence of implementing Pytorch for deep learning (computer vision, NLP, and reinforcement learning), along with the core theory behind these algorithms. If you have some knowledge of deep learning and Tensorflow and want to get started with Pytorch - this is the course for you!
조계홍
October 24, 2023
불필요한 반복을 최소한으로 줄이고 진짜 중요한 것만 전달하는 방식이 좋았음. 다만, 텐서플로우에 비해서 파이토치 강의는 응용이나 실제 문제에 대한 대응이 거의 없이 기본기에 매우 충실한 방식임. 진짜 딱 비기너 클래스를 위한 강의임. 근데 실제 융합연구 등에 써먹을 만한 코드는 다른 강의 듣는게 더 나을거임.
Mohammad
April 12, 2023
It was a really great experience even with some ups and downs. The course is complete and clear but I understand it will still take time to digest fully.
Argha
March 13, 2023
Really in-depth, a lot of useful info and projects, especially for beginners and some real life projects and exercises.
Freddy
March 1, 2023
Yes, the course was really good and interactive and the instructor is amazing! The only feedback I can think of is it would be good to have a cheat sheet or glossary with all the formulas and models we covered and the applications of each for ease of access.
Rui
December 8, 2022
For me I've done some basic practicing with GNN, but found I need to brush up a bit on various applications in the basics. For now the pipelines used here is quite similar to what I saw before and it was really a good review. So quite much what I was expecting for!
Bill
November 25, 2022
Lazy Programmer has designed a very informative and pragmatic course. I didn't know much of any of the basics of deep learning the videos walked me all the way through to mastering deep learning with PyTorch. Thank you.
Anagha
November 16, 2022
Lazy Programmer is a superb teacher. He has the knowledge, but he also knows how to deliver it to the students. I'm satisfied with the course. Thanks!
Vicky
September 11, 2022
Wow, amazing course. Touches every major application of deep learning with modern PyTorch code. I've enjoyed all the content from beginning to end.
Dharmen
August 30, 2022
I find this course extremely good for learning PyTorch from 0 to advanced level. It has a very good structure and the practice projects are very helpful. Thank you for the amazing materials.
Amjad
August 18, 2022
The course has a good collection of topics. However, such materials require more supporting materials (e.g., pdf). The presentation of the materials is, in my opinion, poor: the tutor shows slides and talks. I would expect such above average-priced course to have summaries for the topics, and presentations in different formats. The course also includes legacy code.
Gönczy
July 25, 2022
The educator has his "style"... but the material is indeed one of the best found on Udemy regarding Deep Learning.
Abraham
July 25, 2022
I learned a lot from the Lazy Programmer, and I've actually purchased the whole in-depth deep learning series. He is an insanely talented instructor. I would happily recommend any of his courses to anyone who wants to learn data science and machine learning.
Adithya
July 18, 2022
Gave a detailed overview of all the API's used regularly in PyTorch. the explanations were precise, yet more than required.
Jan
April 19, 2022
Great course with solid introduction and fundamentals of Pytorch and AI. The coding exercises are important to do to master the material covered in the course.

Coupons

DateDiscountStatus
4/1/202080% OFF
expired
5/3/202080% OFF
expired
9/2/202078% OFF
expired
9/8/202078% OFF
expired
10/30/202078% OFF
expired
12/7/202077% OFF
expired
1/29/202176% OFF
expired
2/12/202175% OFF
expired
3/29/202175% OFF
expired
5/17/202175% OFF
expired
11/14/202175% OFF
expired
12/29/202175% OFF
expired
1/22/202275% OFF
expired
3/26/202427% OFF
working

Charts

Price

PyTorch: Deep Learning and Artificial Intelligence - Price chart

Rating

PyTorch: Deep Learning and Artificial Intelligence - Ratings chart

Enrollment distribution

PyTorch: Deep Learning and Artificial Intelligence - Distribution chart

Related Topics

2734668
udemy ID
1/2/2020
course created date
3/31/2020
course indexed date
Bot
course submited by