**PyTorch: Deep Learning and Artificial Intelligence**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 22.5 Hours | 7.27 GB

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

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.

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

**Table of Contents**

**Introduction**

1 Welcome

2 Overview and Outline

3 Where to get the Code

**Google Colab**

4 Intro to Google Colab, how to use a GPU or TPU for free

5 Uploading your own data to Google Colab

6 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn

**Machine Learning and Neurons**

7 What is Machine Learning

8 Saving and Loading a Model

9 A Short Neuroscience Primer

10 How does a model learn

11 Model With Logits

12 Train Sets vs. Validation Sets vs. Test Sets

13 Regression Basics

14 Regression Code Preparation

15 Regression Notebook

16 Moore’s Law

17 Moore’s Law Notebook

18 Linear Classification Basics

19 Classification Code Preparation

20 Classification Notebook

**Feedforward Artificial Neural Networks**

21 Artificial Neural Networks Section Introduction

22 Forward Propagation

23 The Geometrical Picture

24 Activation Functions

25 Multiclass Classification

26 How to Represent Images

27 Code Preparation (ANN)

28 ANN for Image Classification

29 ANN for Regression

**Convolutional Neural Networks**

30 What is Convolution (part 1)

31 CNN for CIFAR-10

32 Data Augmentation

33 Batch Normalization

34 Improving CIFAR-10 Results

35 What is Convolution (part 2)

36 What is Convolution (part 3)

37 Convolution on Color Images

38 CNN Architecture

39 CNN Code Preparation (part 1)

40 CNN Code Preparation (part 2)

41 CNN Code Preparation (part 3)

42 CNN for Fashion MNIST

**Recurrent Neural Networks, Time Series, and Sequence Data**

43 Sequence Data

44 GRU and LSTM (pt 2)

45 A More Challenging Sequence

46 RNN for Image Classification (Theory)

47 RNN for Image Classification (Code)

48 Stock Return Predictions using LSTMs (pt 1)

49 Stock Return Predictions using LSTMs (pt 2)

50 Stock Return Predictions using LSTMs (pt 3)

51 Other Ways to Forecast

52 Forecasting

53 Autoregressive Linear Model for Time Series Prediction

54 Proof that the Linear Model Works

55 Recurrent Neural Networks

56 RNN Code Preparation

57 RNN for Time Series Prediction

58 Paying Attention to Shapes

59 GRU and LSTM (pt 1)

**Natural Language Processing (NLP)**

60 Embeddings

61 Neural Networks with Embeddings

62 Text Preprocessing (pt 1)

63 Text Preprocessing (pt 2)

64 Text Preprocessing (pt 3)

65 Text Classification with LSTMs

66 CNNs for Text

67 Text Classification with CNNs

68 VIP Making Predictions with a Trained NLP Model

**Recommender Systems**

69 Recommender Systems with Deep Learning Theory

70 Recommender Systems with Deep Learning Code Preparation

71 Recommender Systems with Deep Learning Code (pt 1)

72 Recommender Systems with Deep Learning Code (pt 2)

73 VIP Making Predictions with a Trained Recommender Model

**Transfer Learning for Computer Vision**

74 Transfer Learning Theory

75 Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)

76 Large Datasets

77 Approaches to Transfer Learning

78 Transfer Learning Code (pt 1)

79 Transfer Learning Code (pt 2)

**GANs (Generative Adversarial Networks)**

80 GAN Theory

81 GAN Code Preparation

82 GAN Code

**Deep Reinforcement Learning (Theory)**

83 Deep Reinforcement Learning Section Introduction

84 Epsilon-Greedy

85 Q-Learning

86 Deep Q-Learning DQN (pt 1)

87 Deep Q-Learning DQN (pt 2)

88 How to Learn Reinforcement Learning

89 Elements of a Reinforcement Learning Problem

90 States, Actions, Rewards, Policies

91 Markov Decision Processes (MDPs)

92 The Return

93 Value Functions and the Bellman Equation

94 What does it mean to “learn”

95 Solving the Bellman Equation with Reinforcement Learning (pt 1)

96 Solving the Bellman Equation with Reinforcement Learning (pt 2)

**Stock Trading Project with Deep Reinforcement Learning**

97 Reinforcement Learning Stock Trader Introduction

98 Data and Environment

99 Replay Buffer

100 Program Design and Layout

101 Code pt 1

102 Code pt 2

103 Code pt 3

104 Code pt 4

105 Reinforcement Learning Stock Trader Discussion

**VIP Uncertainty Estimation**

106 Custom Loss and Estimating Prediction Uncertainty

107 Estimating Prediction Uncertainty Code

**VIP Facial Recognition**

108 Facial Recognition Section Introduction

109 Facial Recognition Section Summary

110 Siamese Networks

111 Code Outline

112 Loading in the data

113 Splitting the data into train and test

114 Converting the data into pairs

115 Generating Generators

116 Creating the model and loss

117 Accuracy and imbalanced classes

**In-Depth Loss Functions**

118 Mean Squared Error

119 Binary Cross Entropy

120 Categorical Cross Entropy

**In-Depth Gradient Descent**

121 Gradient Descent

122 Stochastic Gradient Descent

123 Momentum

124 Variable and Adaptive Learning Rates

125 Adam

**Extras**

126 Links To Colab Notebooks

127 Links to VIP Notebooks

**Setting up your Environment**

128 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

129 Windows-Focused Environment Setup 2018

130 Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer

**Appendix FAQ**

131 What is the Appendix

132 Is this for Beginners or Experts Academic or Practical Fast or slow-paced

133 How to Code Yourself (part 1)

134 How to Code Yourself (part 2)

135 Proof that using Jupyter Notebook is the same as not using it

136 How to Succeed in this Course (Long Version)

137 What order should I take your courses in (part 1)

138 What order should I take your courses in (part 2)

139 BONUS Where to get discount coupons and FREE deep learning material

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