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2020-12-10 21:18:31
30-Day Money-Back Guarantee

This course includes:

  • 22.5 hours on-demand video
  • 2 articles
  • Full lifetime access
  • Access on mobile and TV
Development Data Science Deep Learning

PyTorch: Deep Learning and Artificial Intelligence

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Bestseller
Rating: 4.7 out of 54.7 (489 ratings)
3,192 students
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 12/2020
English
English [Auto]
30-Day Money-Back Guarantee

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

21 sections • 140 lectures • 22h 42m total length

  • Preview04:03
  • Preview13:14
  • Where to get the Code
    05:36

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 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 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

  • 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 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)

Who this course is for:

  • Beginners to advanced students who want to learn about deep learning and AI in PyTorch

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer
Lazy Programmer Team
  • 4.6 Instructor Rating
  • 39,314 Reviews
  • 144,781 Students
  • 14 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 105,813 Reviews
  • 418,100 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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