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:
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Generating beautiful, photo-realistic images of people and things that never existed (GANs)
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Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
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Self-driving cars (Computer Vision)
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Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
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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:
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Natural Language Processing (NLP)
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Recommender Systems
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Transfer Learning for Computer Vision
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Generative Adversarial Networks (GANs)
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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?:
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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
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Every line of code explained in detail – email me any time if you disagree
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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
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Not afraid of university-level math – get important details about algorithms that other courses leave out
Getting Set Up
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
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11What is Machine Learning?
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12Regression Basics
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13Regression Code Preparation
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14Regression Notebook
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15Moore's Law
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16Moore's Law Notebook
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17Linear Classification Basics
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18Classification Code Preparation
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19Classification Notebook
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20Saving and Loading a Model
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21A Short Neuroscience Primer
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22How does a model "learn"?
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23Model With Logits
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24Train Sets vs. Validation Sets vs. Test Sets
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25Suggestion Box
Convolutional Neural Networks
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26Artificial Neural Networks Section Introduction
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27Forward Propagation
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28The Geometrical Picture
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29Activation Functions
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30Multiclass Classification
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31How to Represent Images
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32Color Mixing Clarification
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33Code Preparation (ANN)
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34ANN for Image Classification
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35ANN for Regression
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36How to Choose Hyperparameters
Recurrent Neural Networks, Time Series, and Sequence Data
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37What is Convolution? (part 1)
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38What is Convolution? (part 2)
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39What is Convolution? (part 3)
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40Convolution on Color Images
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41CNN Architecture
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42CNN Code Preparation (part 1)
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43CNN Code Preparation (part 2)
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44CNN Code Preparation (part 3)
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45CNN for Fashion MNIST
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46CNN for CIFAR-10
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47Data Augmentation
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48Batch Normalization
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49Improving CIFAR-10 Results
Natural Language Processing (NLP)
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50Sequence Data
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51Forecasting
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52Autoregressive Linear Model for Time Series Prediction
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53Proof that the Linear Model Works
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54Recurrent Neural Networks
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55RNN Code Preparation
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56RNN for Time Series Prediction
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57Paying Attention to Shapes
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58GRU and LSTM (pt 1)
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59GRU and LSTM (pt 2)
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60A More Challenging Sequence
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61RNN for Image Classification (Theory)
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62RNN for Image Classification (Code)
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63Stock Return Predictions using LSTMs (pt 1)
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64Stock Return Predictions using LSTMs (pt 2)
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65Stock Return Predictions using LSTMs (pt 3)
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66Other Ways to Forecast
Recommender Systems
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67Embeddings
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68Neural Networks with Embeddings
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69Text Preprocessing Concepts
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70Beginner Blues - PyTorch NLP Version
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71(Legacy) Text Preprocessing Code Preparation
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72(Legacy) Text Preprocessing Code Example
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73Text Classification with LSTMs (V2)
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74CNNs for Text
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75Text Classification with CNNs (V2)
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76(Legacy) VIP: Making Predictions with a Trained NLP Model
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77VIP: Making Predictions with a Trained NLP Model (V2)