PyTorch: Deep Learning and Artificial Intelligence
4.7 (268 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,064 students enrolled

PyTorch: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Bestseller
4.7 (268 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
2,064 students enrolled
Last updated 7/2020
English
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Price: $199.99
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This course includes
  • 22.5 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 140 lectures 22:42:15
+ Google Colab
3 lectures 34:57
Intro to Google Colab, how to use a GPU or TPU for free
12:33
Uploading your own data to Google Colab
13:12
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
09:12
+ Machine Learning and Neurons
15 lectures 02:32:29
What is Machine Learning?
14:26
Regression Basics
14:39
Regression Code Preparation
11:45
Regression Notebook
13:14
Moore's Law
06:57
Moore's Law Notebook
13:51
Linear Classification Basics
15:06
Classification Code Preparation
06:56
Classification Notebook
12:00
Saving and Loading a Model
05:21
A Short Neuroscience Primer
09:51
How does a model "learn"?
10:50
Model With Logits
04:18
Train Sets vs. Validation Sets vs. Test Sets
10:12
Suggestion Box
03:03
+ Feedforward Artificial Neural Networks
9 lectures 01:49:01
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
09:39
How to Represent Images
12:21
Code Preparation (ANN)
14:57
ANN for Image Classification
18:28
ANN for Regression
10:55
+ Convolutional Neural Networks
13 lectures 02:22:13
What is Convolution? (part 1)
16:38
What is Convolution? (part 2)
05:56
What is Convolution? (part 3)
06:41
Convolution on Color Images
16:08
CNN Architecture
20:53
CNN Code Preparation (part 1)
16:55
CNN Code Preparation (part 2)
08:00
CNN Code Preparation (part 3)
05:40
CNN for Fashion MNIST
11:32
CNN for CIFAR-10
08:05
Data Augmentation
09:45
Batch Normalization
05:14
Improving CIFAR-10 Results
10:46
+ Recurrent Neural Networks, Time Series, and Sequence Data
17 lectures 03:05:32
Sequence Data
22:14
Forecasting
10:58
Autoregressive Linear Model for Time Series Prediction
12:15
Proof that the Linear Model Works
04:12
Recurrent Neural Networks
21:31
RNN Code Preparation
13:49
RNN for Time Series Prediction
09:29
Paying Attention to Shapes
09:33
GRU and LSTM (pt 1)
16:09
GRU and LSTM (pt 2)
11:45
A More Challenging Sequence
10:28
RNN for Image Classification (Theory)
04:41
RNN for Image Classification (Code)
02:48
Stock Return Predictions using LSTMs (pt 1)
12:24
Stock Return Predictions using LSTMs (pt 2)
06:16
Stock Return Predictions using LSTMs (pt 3)
11:46
Other Ways to Forecast
05:14
+ Natural Language Processing (NLP)
9 lectures 01:23:44
Embeddings
13:12
Neural Networks with Embeddings
03:45
Text Preprocessing (pt 1)
13:33
Text Preprocessing (pt 2)
11:53
Text Preprocessing (pt 3)
07:53
Text Classification with LSTMs
08:55
CNNs for Text
12:07
Text Classification with CNNs
04:49
VIP: Making Predictions with a Trained NLP Model
07:37
+ Recommender Systems
5 lectures 46:18
Recommender Systems with Deep Learning Theory
10:26
Recommender Systems with Deep Learning Code Preparation
09:38
Recommender Systems with Deep Learning Code (pt 1)
08:52
Recommender Systems with Deep Learning Code (pt 2)
12:31
VIP: Making Predictions with a Trained Recommender Model
04:51
+ Transfer Learning for Computer Vision
6 lectures 41:35
Transfer Learning Theory
08:12
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
04:05
Large Datasets
07:11
2 Approaches to Transfer Learning
04:51
Transfer Learning Code (pt 1)
09:36
Transfer Learning Code (pt 2)
07:40
+ GANs (Generative Adversarial Networks)
3 lectures 31:42
GAN Theory
16:03
GAN Code Preparation
06:18
GAN Code
09:21
Requirements
  • Know how to code in Python and Numpy
  • For the theoretical parts (optional), understand derivatives and probability
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!

Who this course is for:
  • Beginners to advanced students who want to learn about deep learning and AI in PyTorch