PyTorch: Deep Learning and Artificial Intelligence

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

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Course Data

Lectures: 160
Length: 25h 18m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

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 Tensorflow 2.0, 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!

Testimonials and Success Stories


I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...

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I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...

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By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.

Lectures

Introduction

2 Lectures · 17min
  1. Welcome (04:04) (FREE preview available)
  2. Overview and Outline (13:14)

Getting Set Up

2 Lectures · 12min
  1. Where to get the code and data - instant access (01:42)
  2. How to use Github & Extra Coding Tips (Optional) (11:12)

Google Colab

4 Lectures · 36min
  1. Intro to Google Colab, how to use a GPU or TPU for free (12:33)
  2. Uploading your own data to Google Colab (11:42)
  3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn? (08:55)
  4. Temporary 403 Errors (02:58)

Machine Learning and Neurons

17 Lectures · 02hr 37min
  1. What is Machine Learning? (14:26)
  2. Regression Basics (14:39)
  3. Regression Code Preparation (11:45)
  4. Regression Notebook (13:14)
  5. Moore's Law (06:57)
  6. Moore's Law Notebook (13:51)
  7. Linear Regression Exercise: Real Estate Predictions (02:33)
  8. Linear Classification Basics (15:06)
  9. Classification Code Preparation (06:56)
  10. Classification Notebook (12:00)
  11. Logistic Regression Exercise: Predicting Diabetes Onset (02:34)
  12. Saving and Loading a Model (05:21)
  13. A Short Neuroscience Primer (09:51)
  14. How does a model "learn"? (10:50)
  15. Model With Logits (04:18)
  16. Train Sets vs. Validation Sets vs. Test Sets (10:12)
  17. Suggestion Box (03:10)

Feedforward Artificial Neural Networks

12 Lectures · 02hr 52min
  1. Artificial Neural Networks Section Introduction (06:00)
  2. Forward Propagation (09:40)
  3. The Geometrical Picture (09:43)
  4. Activation Functions (17:18)
  5. Multiclass Classification (09:39)
  6. How to Represent Images (12:21)
  7. Color Mixing Clarification (55:00)
  8. Code Preparation (ANN) (14:57)
  9. ANN for Image Classification (18:28)
  10. ANN for Regression (10:55)
  11. Exercise: E. Coli Protein Localization Sites (02:21)
  12. How to Choose Hyperparameters (06:25)

Convolutional Neural Networks (CNNs)

14 Lectures · 02hr 24min
  1. What is Convolution? (part 1) (16:38)
  2. What is Convolution? (part 2 - Pattern Finding) (05:56)
  3. What is Convolution? (part 3 - Weight Sharing) (06:41)
  4. Convolution on Color Images (15:58)
  5. CNN Architecture (20:53)
  6. CNN Code Preparation (part 1) (17:42)
  7. CNN Code Preparation (part 2) (08:00)
  8. CNN Code Preparation (part 3) (05:40)
  9. CNN for Fashion MNIST (11:32)
  10. CNN for CIFAR-10 (08:05)
  11. Data Augmentation (09:45)
  12. Batch Normalization (05:14)
  13. Improving CIFAR-10 Results (10:46)
  14. Exercise: Facial Expression Recognition (01:35)

Recurrent Neural Networks (RNNs), Time Series, and Sequence Data

18 Lectures · 03hr 08min
  1. Sequence Data (22:14)
  2. Forecasting (10:58)
  3. Autoregressive Linear Model for Time Series Prediction (12:15)
  4. Proof that the Linear Model Works (04:12)
  5. Recurrent Neural Networks (21:31)
  6. RNN Code Preparation (13:49)
  7. RNN for Time Series Prediction (09:29)
  8. Paying Attention to Shapes (09:33)
  9. GRU and LSTM (pt 1) (17:35)
  10. GRU and LSTM (pt 2) (11:45)
  11. A More Challenging Sequence (10:28)
  12. RNN for Image Classification (Theory) (04:41)
  13. RNN for Image Classification (Code) (02:48)
  14. Stock Return Predictions using LSTMs (pt 1) (12:24)
  15. Stock Return Predictions using LSTMs (pt 2) (06:16)
  16. Stock Return Predictions using LSTMs (pt 3) (11:45)
  17. Other Ways to Forecast (05:14)
  18. Exercise: More Forecasting (01:52)

Natural Language Processing (NLP)

14 Lectures · 02hr 05min
  1. Embeddings (13:12)
  2. Neural Networks with Embeddings (03:45)
  3. Text Preprocessing Concepts (13:33)
  4. Beginner Blues - PyTorch NLP Version (10:36)
  5. (Legacy) Text Preprocessing Code Preparation (11:53)
  6. (Legacy) Text Preprocessing Code Example (07:53)
  7. (Legacy) Text Classification with LSTMs (V1) (08:55)
  8. Text Classification with LSTMs (V2) (17:42)
  9. CNNs for Text (12:07)
  10. (Legacy) Text Classification with CNNs (V1) (04:49)
  11. Text Classification with CNNs (V2) (07:15)
  12. (Legacy) VIP: Making Predictions with a Trained NLP Model (07:37)
  13. VIP: Making Predictions with a Trained NLP Model (V2) (04:21)
  14. Exercise: Sentiment Analysis (02:01)

Recommender Systems

6 Lectures · 47min
  1. Recommender Systems with Deep Learning Theory (10:26)
  2. Recommender Systems with Deep Learning Code Preparation (09:38)
  3. Recommender Systems with Deep Learning Code (pt 1) (08:52)
  4. Recommender Systems with Deep Learning Code (pt 2) (12:31)
  5. VIP: Making Predictions with a Trained Recommender Model (04:51)
  6. Exercise: Book Recommendations (01:13)

Transfer Learning for Computer Vision

7 Lectures · 43min
  1. Transfer Learning Theory (08:12)
  2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet) (04:05)
  3. Large Datasets (07:11)
  4. 2 Approaches to Transfer Learning (04:51)
  5. Transfer Learning Code (pt 1) (09:36)
  6. Transfer Learning Code (pt 2) (07:40)
  7. Exercise: Transfer Learning (01:28)

GANs (Generative Adversarial Networks)

4 Lectures · 34min
  1. GAN Theory (16:03)
  2. GAN Code Preparation (06:18)
  3. GAN Code (09:21)
  4. Exercise: DCGAN (Deep Convolutional GAN) (02:54)

Deep Reinforcement Learning (Theory)

14 Lectures · 02hr 21min
  1. Reinforcement Learning Section Introduction (06:34)
  2. Elements of a Reinforcement Learning Problem (20:18)
  3. States, Actions, Rewards, Policies (09:24)
  4. Markov Decision Processes (MDPs) (10:07)
  5. The Return (04:56)
  6. Value Functions and the Bellman Equation (09:53)
  7. What does it mean to “learn”? (07:18)
  8. Solving the Bellman Equation with Reinforcement Learning (pt 1) (09:49)
  9. Solving the Bellman Equation with Reinforcement Learning (pt 2) (12:04)
  10. Epsilon-Greedy (06:09)
  11. Q-Learning (14:15)
  12. Deep Q-Learning / DQN (pt 1) (14:05)
  13. Deep Q-Learning / DQN (pt 2) (10:25)
  14. How to Learn Reinforcement Learning (05:56)

Stock Trading Project with Deep Reinforcement Learning

10 Lectures · 01hr 08min
  1. Reinforcement Learning Stock Trader Introduction (05:13)
  2. Data and Environment (12:22)
  3. Replay Buffer (05:40)
  4. Program Design and Layout (06:56)
  5. Code pt 1 (09:22)
  6. Code pt 2 (09:40)
  7. Code pt 3 (06:54)
  8. Code pt 4 (07:25)
  9. Reinforcement Learning Stock Trader Discussion (03:36)
  10. Exercise: Personalized Stock Trading Bot (01:44)

VIP: Uncertainty Estimation

2 Lectures · 16min
  1. Custom Loss and Estimating Prediction Uncertainty (09:36)
  2. Estimating Prediction Uncertainty Code (07:12)

VIP: Facial Recognition

10 Lectures · 55min
  1. Facial Recognition Section Introduction (03:39)
  2. Siamese Networks (10:17)
  3. Code Outline (05:05)
  4. Loading in the data (05:52)
  5. Splitting the data into train and test (04:27)
  6. Converting the data into pairs (05:04)
  7. Generating Generators (05:06)
  8. Creating the model and loss (04:28)
  9. Accuracy and imbalanced classes (07:48)
  10. Facial Recognition Section Summary (03:32)

In-Depth: Loss Functions

3 Lectures · 23min
  1. Mean Squared Error (09:11)
  2. Binary Cross Entropy (05:58)
  3. Categorical Cross Entropy (08:06)

In-Depth: Gradient Descent

6 Lectures · 54min
  1. Gradient Descent (07:52)
  2. Stochastic Gradient Descent (04:36)
  3. Momentum (06:11)
  4. Variable and Adaptive Learning Rates (11:46)
  5. Adam Optimization (pt 1) (13:15)
  6. Adam Optimization (pt 2) (11:14)

Setting Up Your Environment (Appendix/FAQ by Student Request)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

6 Lectures · 01hr 05min
  1. Beginner's Coding Tips (13:22)
  2. How to Code Yourself (part 1) (15:55)
  3. How to Code Yourself (part 2) (09:24)
  4. Proof that using Jupyter Notebook is the same as not using it (12:29)
  5. Python 2 vs Python 3 (04:38)
  6. Is Theano Dead? (10:04)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:49)

Extras

  • Data Links
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