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.

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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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