PyTorch for Deep Learning and Computer Vision

PyTorch for Deep Learning and Computer Vision
PyTorch for Deep Learning and Computer Vision
English | MP4 | AVC 1920×1280 | AAC 44KHz 2ch | 12h 32m | 3.31 GB

Learn to build highly sophisticated Deep Learning and Computer Vision Applications with PyTorch

PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. Learn & Master Deep Learning with PyTorch in this fun and exciting course with top instructor Rayan Slim. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a “learn by doing” style to create this amazing course. You’ll go from beginner to Deep Learning expert and your instructor will complete each task with you step by step on screen. By the end of the course, you will have built state-of-the art Deep Learning and Computer Vision applications with PyTorch. The projects built in this course will impress even the most senior developers and ensure you have hands on skills that you can bring to any project or company.

This course will show you to:

  • Learn how to work with the tensor data structure
  • Implement Machine and Deep Learning applications with PyTorch
  • Build neural networks from scratch
  • Build complex models through the applied theme of advanced imagery and Computer Vision
  • Learn to solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models
  • Use style transfer to build sophisticated AI applications that are able to seamlessly recompose images in the style of other images.

This course is meant to take you from the complete basics to building state-of-the-art Deep Learning and Computer Vision applications with PyTorch.

What You Will Learn

  • Implement Machine and Deep Learning applications with PyTorch
  • Build Neural Networks from scratch
  • Build complex models through the applied theme of Advanced Imagery and Computer Vision
  • Solve complex problems in Computer Vision by harnessing highly sophisticated pre-trained models
  • Use style transfer to build sophisticated AI applications
Table of Contents

1 Introduction
2 Finding the codes (Github)
3 A Look at the Projects
4 Intro
5 1 Dimensional Tensors
6 Vector Operations
7 2 Dimensional Tensors
8 Slicing 3D Tensors
9 Matrix Multiplication
10 Gradient with PyTorch
11 Outro
12 Intro
13 Making Predictions
14 Linear Class
15 Custom Modules
16 Creating Dataset
17 Loss Function
18 Gradient Descent
19 Mean Squared Error
20 Training – Code Implementation
21 Outro
22 Intro
23 What is Deep Learning
24 Creating Dataset
25 Perceptron Model
26 Model Setup
27 Model Training
28 Model Testing
29 Outro
30 Intro
31 Non-Linear Boundaries
32 Architecture
33 Feedforward Process
34 Error Function
35 Backpropagation
36 Code Implementation
37 Testing Model
38 Outro
39 Intro
40 MNIST Dataset
41 Training and Test Datasets
42 Image Transforms
43 Neural Network Implementation
44 Neural Network Validation
45 Final Tests
46 A note on adjusting batch size
47 Outro
48 Convolutions and MNIST
49 Convolutional Layer
50 Convolutions II
51 Pooling
52 Fully Connected Network
53 Neural Network Implementation with PyTorch
54 Model Training with PyTorch
55 The CIFAR 10 Dataset
56 Testing LeNet
57 Hyperparameter Tuning
58 Data Augmentation
59 Pre-trained Sophisticated Models
60 AlexNet and VGG16
61 VGG 19
62 Image Transforms
63 Feature Extraction
64 The Gram Matrix
65 Optimization
66 Style Transfer with Video
67 Overview
68 Anaconda Installation (Mac)
69 Anaconda Installation Windows
70 Jupyter Notebooks
71 Arithmetic Operators
72 Variables
73 Numeric Data Types
74 String
75 Booleans
76 Methods
77 Lists
78 Slicing
79 Membership Operator
80 Mutability
81 Mutability II
82 Common Functions & Methods
83 Tuples
84 Sets
85 Dictionaries
86 Compound Data Structures
87 Part 1 – Outro
88 Part 2 – Control Flow
89 If, else
90 elseif
91 Complex Comparisons
92 For Loops
93 For Loops II
94 While Loops
95 Break
96 Part 2 – Outro
97 Part 3 – Functions
98 Functions
99 Scope
100 Doc Strings
101 Lambda and Higher Order Functions
102 Part 3 – Outro
103 Overview
104 Arrays vs Lists
105 Multidimensional Arrays
106 One Dimensional Slicing
107 Reshaping
108 Multidimensional Slicing
109 Manipulating Array Shapes
110 Matrix Multiplication
111 Stacking
112 Outro
113 Softmax
114 Cross Entropy