PyTorch for Deep Learning and Computer Vision
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
- 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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All Course Lessons (79)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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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