PyTorch for Deep Learning and Computer Vision

10h 20m 51s
English
Paid

Course description

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.

Read more about the course

Deep Learning jobs command some of the highest salaries in the development world. 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.

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.

No experience required. This course is designed to take students with no programming/mathematics experience to accomplished Deep Learning developers.

This course also comes with all the source code and friendly support in the Q&A area.

Who this course is for:

  • Anyone with an interest in Deep Learning and Computer Vision

  • Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence

  • Entrepreneurs with an interest in working on some of the most cutting edge technologies

  • All skill levels are welcome!

Requirements:

  • No experience is required

Who this course is for:
  • Anyone with an interest in Deep Learning and Computer Vision
  • Anyone (no matter the skill level) who wants to transition into the field of Artificial Intelligence
  • Entrepreneurs with an interest in working on some of the most cutting edge technologies
  • All skill levels are welcome!

What you'll 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

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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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