CF

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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Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling in this course, you should have a basic understanding of programming concepts and some experience with Python. Familiarity with fundamental mathematical concepts such as vectors and matrices will also be beneficial, as the course covers topics like 1 Dimensional Tensors and Matrix Multiplication.
What types of projects will I build in this course?
Throughout the course, you will build state-of-the-art Deep Learning and Computer Vision applications. This includes developing neural networks from scratch, implementing complex models for advanced imagery, and solving Computer Vision problems using pre-trained models. Additionally, you will work on style transfer projects, which involve recomposing images in the style of other images.
Who is the target audience for this course?
This course is designed for individuals seeking to transition into the field of Deep Learning and Computer Vision. It's suitable for beginners looking to build foundational skills as well as for those who want to deepen their expertise in using PyTorch for developing sophisticated AI applications.
How does this course compare in depth and scope to similar courses?
The course offers a comprehensive journey from the basics of tensor operations and neural network implementation to advanced topics like pre-trained models and convolutional networks. With 79 lessons, it covers both foundational and sophisticated aspects of PyTorch, making it more extensive than introductory courses but accessible to those new to the field.
What specific tools or platforms will be used in this course?
The primary tool used in this course is PyTorch, a leading framework for Deep Learning. You will learn to implement neural networks, perform gradient calculations, and utilize pre-trained models within the PyTorch environment. The course also includes exercises related to datasets like MNIST and CIFAR-10.
What topics are not covered in this course?
The course does not cover non-PyTorch frameworks such as TensorFlow or Keras. Additionally, it doesn't delve into non-vision related applications of Deep Learning, such as Natural Language Processing or Reinforcement Learning, as the focus is primarily on Computer Vision and PyTorch-based solutions.
What is the time commitment required for this course?
The course comprises 79 lessons, each designed to build on the previous one. While the total runtime is not specified, students should allocate sufficient time to engage with the 'learn by doing' exercises and projects that reinforce the concepts taught in each session for a comprehensive learning experience.