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 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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# Title Duration
1 Introduction 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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