English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 174 Lessons (26h 42m) | 7.54 GB

Learn PyTorch from scratch! This PyTorch course is your step-by-step guide to developing your own deep learning models using PyTorch. You’ll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you’ll have the skills and portfolio to get hired as a Deep Learning Engineer.

Learn PyTorch. Become a Deep Learning Engineer. Get Hired.

We can guarantee (with, like, 99.57% confidence) that this is the most comprehensive, modern, and up-to-date course you will find to learn PyTorch and the cutting-edge field of Deep Learning. Daniel takes you step-by-step from an absolute beginner to becoming a master of Deep Learning with PyTorch.

WHAT YOU’LL LEARN

- Everything from getting started with using PyTorch to building your own real-world models
- Why PyTorch is a fantastic way to start working in machine learning
- Understand how to integrate Deep Learning into tools and applications
- Create and utilize machine learning algorithms just like you would write a Python program
- Build and deploy your own custom trained PyTorch neural network accessible to the public
- How to take data, build a ML algorithm to find patterns, and then use that algorithm as an AI to enhance your applications
- Master deep learning and become a top candidate for recruiters seeking Deep Learning Engineers
- To expand your Machine Learning and Deep Learning skills and toolkit
- The skills you need to become a Deep Learning Engineer and get hired with a chance of making US$100,000+ / year

## Table of Contents

1 PyTorch for Deep Learning

2 Course Welcome and What Is Deep Learning

3 Why Use Machine Learning or Deep Learning

4 The Number 1 Rule of Machine Learning and What Is Deep Learning Good For

5 Machine Learning vs. Deep Learning

6 Anatomy of Neural Networks

7 Different Types of Learning Paradigms

8 What Can Deep Learning Be Used For

9 What Is and Why PyTorch

10 What Are Tensors

11 What We Are Going To Cover With PyTorch

12 How To and How Not To Approach This Course

13 Important Resources For This Course

14 Getting Setup to Write PyTorch Code

15 Introduction to PyTorch Tensors

16 Creating Random Tensors in PyTorch

17 Creating Tensors With Zeros and Ones in PyTorch

18 Creating a Tensor Range and Tensors Like Other Tensors

19 Dealing With Tensor Data Types

20 Getting Tensor Attributes

21 Manipulating Tensors (Tensor Operations)

22 Matrix Multiplication (Part 1)

23 Matrix Multiplication (Part 2): The Two Main Rules of Matrix Multiplication

24 Matrix Multiplication (Part 3): Dealing With Tensor Shape Errors

25 Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation)

26 Finding The Positional Min and Max of Tensors

27 Reshaping, Viewing and Stacking Tensors

28 Squeezing, Unsqueezing and Permuting Tensors

29 Selecting Data From Tensors (Indexing)

30 PyTorch Tensors and NumPy

31 PyTorch Reproducibility (Taking the Random Out of Random)

32 Different Ways of Accessing a GPU in PyTorch

33 Setting up Device Agnostic Code and Putting Tensors On and Off the GPU

34 PyTorch Fundamentals: Exercises and Extra-Curriculum

35 Introduction and Where You Can Get Help

36 Getting Setup and What We Are Covering

37 Creating a Simple Dataset Using the Linear Regression Formula

38 Splitting Our Data Into Training and Test Sets

39 Building a function to Visualize Our Data

40 Creating Our First PyTorch Model for Linear Regression

41 Breaking Down What’s Happening in Our PyTorch Linear regression Model

42 Discussing Some of the Most Important PyTorch Model Building Classes

43 Checking Out the Internals of Our PyTorch Model

44 Making Predictions With Our Random Model Using Inference Mode

45 Training a Model Intuition (The Things We Need)

46 Setting Up an Optimizer and a Loss Function

47 PyTorch Training Loop Steps and Intuition

48 Writing Code for a PyTorch Training Loop

49 Reviewing the Steps in a Training Loop Step by Step

50 Running Our Training Loop Epoch by Epoch and Seeing What Happens

51 Writing Testing Loop Code and Discussing What’s Happening Step by Step

52 Reviewing What Happens in a Testing Loop Step by Step

53 Writing Code to Save a PyTorch Model

54 Writing Code to Load a PyTorch Model

55 Setting Up to Practice Everything We Have Done Using Device-Agnostic Code

56 Putting Everything Together (Part 1): Data

57 Putting Everything Together (Part 2): Building a Model

58 Putting Everything Together (Part 3): Training a Model

59 Putting Everything Together (Part 4): Making Predictions With a Trained Model

60 Putting Everything Together (Part 5): Saving and Loading a Trained Model

61 Exercise: Imposter Syndrome

62 PyTorch Workflow Exercises: Extra-Curriculum

63 Introduction to Machine Learning Classification With PyTorch

64 Classification Problem Example: Input and Output Shapes

65 Typical Architecture of a Classification Neural Network (Overview)

66 Making a Toy Classification Dataset

67 Turning Our Data into Tensors and Making a Training and Test Split

68 Laying Out Steps for Modelling and Setting Up Device-Agnostic Code

69 Coding a Small Neural Network to Handle Our Classification Data

70 Making Our Neural Network Visual

71 Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network

72 Going from Model Logits to Prediction Probabilities to Prediction Labels

73 Coding a Training and Testing Optimization Loop for Our Classification Model

74 Writing Code to Download a Helper Function to Visualize Our Models Predictions

75 Discussing Options to Improve a Model

76 Creating a New Model with More Layers and Hidden Units

77 Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better

78 Creating a Straight Line Dataset to See if Our Model is Learning Anything

79 Building and Training a Model to Fit on Straight Line Data

80 Evaluating Our Models Predictions on Straight Line Data

81 Introducing the Missing Piece for Our Classification Model Non-Linearity

82 Building Our First Neural Network with Non-Linearity

83 Writing Training and Testing Code for Our First Non-Linear Model

84 Making Predictions with and Evaluating Our First Non-Linear Model

85 Replicating Non-Linear Activation Functions with Pure PyTorch

86 Putting It All Together (Part 1): Building a Multiclass Dataset

87 Creating a Multi-Class Classification Model with PyTorch

88 Setting Up a Loss Function and Optimizer for Our Multi-Class Model

89 Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model

90 Making Predictions with and Evaluating Our Multi-Class Classification Model

91 Discussing a Few More Classification Metrics

92 PyTorch Classification Exercises and Extra-Curriculum

93 What Is a Computer Vision Problem and What We Are Going to Cover

94 Computer Vision Input and Output Shapes (

95 What Is a Convolutional Neural Network (CNN)

96 Discussing and Importing the Base Computer Vision Libraries in PyTorch

97 Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes

98 Visualizing Random Samples of Data

99 DataLoader Overview Understanding Mini-Batch

100 Turning Our Datasets Into DataLoaders

101 Model 0: Creating a Baseline Model with Two Linear Layers

102 Creating a Loss Function: an Optimizer for Model 0

103 Creating a Function to Time Our Modelling Code

104 Writing Training and Testing Loops for Our Batched Data

105 Writing an Evaluation Function to Get Our Models Results

106 Setup Device-Agnostic Code for Running Experiments on the GPU

107 Model 1: Creating a Model with Non-Linear Functions

108 Mode 1: Creating a Loss Function and Optimizer

109 Turing Our Training Loop into a Function

110 Turing Our Testing Loop into a Function

111 Training and Testing Model 1 with Our Training and Testing Functions

112 Getting a Results Dictionary for Model 1

113 Model 2: Convolutional Neural Networks High Level Overview

114 Model 2: Coding Our First Convolutional Neural Network with PyTorch

115 Model 2: Breaking Down Conv2D Step by Step

116 Model 2: Breaking Down MaxPool2D Step by Step

117 Mode 2: Using a Trick to Find the Input and Output Shapes of Each of Our Layers

118 Model 2: Setting Up a Loss Function and Optimizer

119 Model 2: Training Our First CNN and Evaluating Its Results

120 Comparing the Results of Our Modelling Experiments

121 Making Predictions on Random Test Samples with the Best Trained Model

122 Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them

123 Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix

124 Evaluating Our Best Models Predictions with a Confusion Matrix

125 Saving and Loading Our Best Performing Model

126 Recapping What We Have Covered and Exercises and Extra-Curriculum

127 What Is a Custom Dataset and What We Are Going to Cover

128 Importing PyTorch and Setting Up Device-Agnostic Code

129 Downloading a Custom Dataset of Pizza, Steak and Sushi Images

130 Becoming One With the Data (Part 1): Exploring the Data Format

131 Becoming One With the Data (Part 2): Visualizing a Random Image

132 Becoming One With the Data (Part 3): Visualizing a Random Image with Matplotlib

133 Transforming Data (Part 1): Turning Images Into Tensors

134 Transforming Data (Part 2): Visualizing Transformed Images

135 Loading All of Our Images and Turning Them Into Tensors With ImageFolder

136 Visualizing a Loaded Image From the Train Dataset

137 Turning Our Image Datasets into PyTorch DataLoaders

138 Creating a Custom Dataset Class in PyTorch High Level Overview

139 Creating a Helper Function to Get Class Names From a Directory

140 Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images

141 Compare Our Custom Dataset Class to the Original ImageFolder Class

142 Writing a Helper Function to Visualize Random Images from Our Custom Dataset

143 Turning Our Custom Datasets Into DataLoaders

144 Exploring State of the Art Data Augmentation With Torchvision Transforms

145 Building a Baseline Model (Part 1): Loading and Transforming Data

146 Building a Baseline Model (Part 2): Replicating Tiny VGG from Scratch

147 Building a Baseline Model (Part 3): Doing a Forward Pass to Test Our Model Shapes

148 Using the Torchinfo Package to Get a Summary of Our Model

149 Creating Training and Testing loop Functions

150 Creating a Train Function to Train and Evaluate Our Models

151 Training and Evaluating Model 0 With Our Training Functions

152 Plotting the Loss Curves of Model 0

153 Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each

154 Creating Augmented Training Datasets and DataLoaders for Model 1

155 Constructing and Training Model 1

156 Plotting the Loss Curves of Model 1

157 Plotting the Loss Curves of All of Our Models Against Each Other

158 Predicting on Custom Data (Part 1): Downloading an Image

159 Predicting on Custom Data (Part2): Loading In a Custom Image With PyTorch

160 Predicting on Custom Data (Part 3): Getting Our Custom Image Into the Right Format

161 Predicting on Custom Data (Part 4): Turning Our Models Raw Outputs Into Prediction Labels

162 Predicting on Custom Data (Part 5): Putting It All Together

163 Summary of What We Have Covered Plus Exercises and Extra-Curriculum

164 What Is Going Modular and What We Are Going to Cover

165 Going Modular Notebook (Part 1): Running It End to End

166 Downloading a Dataset

167 Writing the Outline for Our First Python Script to Setup the Data

168 Creating a Python Script to Create Our PyTorch DataLoaders

169 Turning Our Model Building Code into a Python Script

170 Turning Our Model Training Code into a Python Script

171 Turning Our Utility Function to Save a Model into a Python Script

172 Creating a Training Script to Train Our Model in One Line of Code

173 Going Modular Summary Exercises and Extra-Curriculum

174 Thank You

Resolve the captcha to access the links!