CourseFlix

PyTorch for Deep Learning and Computer Vision

10h 20m 51s
English
Paid

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 ease of use when building Deep Learning models.

Course Overview

Deep Learning jobs command some of the highest salaries in the development world. This course is structured to take you from the basics to building state-of-the-art Deep Learning and Computer Vision applications using PyTorch.

Join top instructor Rayan Slim in this exciting course. With over 44,000 students, Rayan's "learn by doing" approach offers an engaging way to master Deep Learning with PyTorch. You'll progress from a beginner to a Deep Learning expert as your instructor guides you step-by-step through each task on screen.

By course completion, you will have developed impressive Deep Learning and Computer Vision applications with PyTorch. These projects will enhance your practical skills and increase your value in any project or company.

What You Will Learn

  • Understand and work with the tensor data structure
  • Implement Machine and Deep Learning applications using PyTorch
  • Build neural networks from scratch
  • Create complex models focused on advanced imagery and Computer Vision
  • Solve challenging Computer Vision problems by leveraging sophisticated pre-trained models
  • Utilize style transfer to develop AI applications that can recompose images in the style of other images

Course Requirements

  • No experience required: This course is designed to develop students from no programming or mathematics experience to accomplished Deep Learning developers.

Who This Course is For

  • Individuals interested in Deep Learning and Computer Vision
  • Those looking to transition into the field of Artificial Intelligence, regardless of skill level
  • Entrepreneurs eager to work with cutting-edge technologies
  • Participants of all skill levels welcome!

Additional Course Benefits

This course includes all the source code and offers friendly support in the Q&A section.

About the Author: Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

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#1: Introduction
All Course Lessons (79)
#Lesson TitleDurationAccess
1
Introduction Demo
01:48
2
Finding the codes (Github)
00:47
3
A Look at the Projects
02:42
4
Intro
00:19
5
1 Dimensional Tensors
08:54
6
Vector Operations
05:24
7
2 Dimensional Tensors
05:31
8
Slicing 3D Tensors
03:04
9
Matrix Multiplication
03:22
10
Gradient with PyTorch
04:24
11
Outro
00:14
12
Intro
00:45
13
Making Predictions
06:16
14
Linear Class
04:30
15
Custom Modules
08:10
16
Creating Dataset
10:36
17
Loss Function
03:34
18
Gradient Descent
04:42
19
Mean Squared Error
03:16
20
Training - Code Implementation
11:37
21
Outro
00:32
22
Intro
00:35
23
What is Deep Learning
01:20
24
Creating Dataset
09:35
25
Perceptron Model
11:57
26
Model Setup
11:23
27
Model Training
10:39
28
Model Testing
05:24
29
Outro
00:24
30
Intro
00:29
31
Non-Linear Boundaries
03:12
32
Architecture
09:07
33
Feedforward Process
07:47
34
Error Function
04:11
35
Backpropagation
05:04
36
Code Implementation
08:50
37
Testing Model
15:22
38
Outro
00:23
39
Intro
00:37
40
MNIST Dataset
05:51
41
Training and Test Datasets
12:40
42
Image Transforms
16:27
43
Neural Network Implementation
30:45
44
Neural Network Validation
12:22
45
Final Tests
13:27
46
A note on adjusting batch size
01:29
47
Outro
00:22
48
Convolutions and MNIST
06:10
49
Convolutional Layer
18:12
50
Convolutions II
08:08
51
Pooling
14:12
52
Fully Connected Network
06:24
53
Neural Network Implementation with PyTorch
12:47
54
Model Training with PyTorch
17:19
55
The CIFAR 10 Dataset
01:45
56
Testing LeNet
09:52
57
Hyperparameter Tuning
07:53
58
Data Augmentation
12:26
59
Pre-trained Sophisticated Models
14:41
60
AlexNet and VGG16
27:35
61
VGG 19
09:46
62
Image Transforms
17:27
63
Feature Extraction
12:10
64
The Gram Matrix
12:02
65
Optimization
25:13
66
Style Transfer with Video
10:07
67
Python Crash Course - Free Access
00:56
68
Overview
00:49
69
Arrays vs Lists
12:04
70
Multidimensional Arrays
11:47
71
One Dimensional Slicing
03:34
72
Reshaping
03:35
73
Multidimensional Slicing
07:21
74
Manipulating Array Shapes
08:18
75
Matrix Multiplication
04:20
76
Stacking
13:51
77
Outro
00:09
78
Softmax
11:47
79
Cross Entropy
08:02
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Course content

79 lessons · 10h 20m 51s
Show all 79 lessons
  1. 1 Introduction 01:48
  2. 2 Finding the codes (Github) 00:47
  3. 3 A Look at the Projects 02:42
  4. 4 Intro 00:19
  5. 5 1 Dimensional Tensors 08:54
  6. 6 Vector Operations 05:24
  7. 7 2 Dimensional Tensors 05:31
  8. 8 Slicing 3D Tensors 03:04
  9. 9 Matrix Multiplication 03:22
  10. 10 Gradient with PyTorch 04:24
  11. 11 Outro 00:14
  12. 12 Intro 00:45
  13. 13 Making Predictions 06:16
  14. 14 Linear Class 04:30
  15. 15 Custom Modules 08:10
  16. 16 Creating Dataset 10:36
  17. 17 Loss Function 03:34
  18. 18 Gradient Descent 04:42
  19. 19 Mean Squared Error 03:16
  20. 20 Training - Code Implementation 11:37
  21. 21 Outro 00:32
  22. 22 Intro 00:35
  23. 23 What is Deep Learning 01:20
  24. 24 Creating Dataset 09:35
  25. 25 Perceptron Model 11:57
  26. 26 Model Setup 11:23
  27. 27 Model Training 10:39
  28. 28 Model Testing 05:24
  29. 29 Outro 00:24
  30. 30 Intro 00:29
  31. 31 Non-Linear Boundaries 03:12
  32. 32 Architecture 09:07
  33. 33 Feedforward Process 07:47
  34. 34 Error Function 04:11
  35. 35 Backpropagation 05:04
  36. 36 Code Implementation 08:50
  37. 37 Testing Model 15:22
  38. 38 Outro 00:23
  39. 39 Intro 00:37
  40. 40 MNIST Dataset 05:51
  41. 41 Training and Test Datasets 12:40
  42. 42 Image Transforms 16:27
  43. 43 Neural Network Implementation 30:45
  44. 44 Neural Network Validation 12:22
  45. 45 Final Tests 13:27
  46. 46 A note on adjusting batch size 01:29
  47. 47 Outro 00:22
  48. 48 Convolutions and MNIST 06:10
  49. 49 Convolutional Layer 18:12
  50. 50 Convolutions II 08:08
  51. 51 Pooling 14:12
  52. 52 Fully Connected Network 06:24
  53. 53 Neural Network Implementation with PyTorch 12:47
  54. 54 Model Training with PyTorch 17:19
  55. 55 The CIFAR 10 Dataset 01:45
  56. 56 Testing LeNet 09:52
  57. 57 Hyperparameter Tuning 07:53
  58. 58 Data Augmentation 12:26
  59. 59 Pre-trained Sophisticated Models 14:41
  60. 60 AlexNet and VGG16 27:35
  61. 61 VGG 19 09:46
  62. 62 Image Transforms 17:27
  63. 63 Feature Extraction 12:10
  64. 64 The Gram Matrix 12:02
  65. 65 Optimization 25:13
  66. 66 Style Transfer with Video 10:07
  67. 67 Python Crash Course - Free Access 00:56
  68. 68 Overview 00:49
  69. 69 Arrays vs Lists 12:04
  70. 70 Multidimensional Arrays 11:47
  71. 71 One Dimensional Slicing 03:34
  72. 72 Reshaping 03:35
  73. 73 Multidimensional Slicing 07:21
  74. 74 Manipulating Array Shapes 08:18
  75. 75 Matrix Multiplication 04:20
  76. 76 Stacking 13:51
  77. 77 Outro 00:09
  78. 78 Softmax 11:47
  79. 79 Cross Entropy 08:02

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Frequently asked questions

What is PyTorch for Deep Learning and Computer Vision about?
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 ease of use when…
Who teaches PyTorch for Deep Learning and Computer Vision?
PyTorch for Deep Learning and Computer Vision is taught by Udemy. You can find more courses by this instructor on the corresponding source page.
How long is PyTorch for Deep Learning and Computer Vision?
PyTorch for Deep Learning and Computer Vision contains 79 lessons with a total runtime of 10 hours 20 minutes. All lessons are available to watch online at your own pace.
Is PyTorch for Deep Learning and Computer Vision free to watch?
PyTorch for Deep Learning and Computer Vision is part of CourseFlix's premium catalog. A CourseFlix subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch PyTorch for Deep Learning and Computer Vision online?
PyTorch for Deep Learning and Computer Vision is available to watch online on CourseFlix at https://courseflix.net/course/pytorch-for-deep-learning-and-computer-vision. The page hosts every lesson with the integrated video player; no download is required.