4.57 out of 5
4.57
282 reviews on Udemy

PyTorch for Deep Learning and Computer Vision

Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch
Instructor:
Rayan Slim
1,907 students enrolled
English [Auto-generated]
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

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.

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!

Introduction

1
Introduction

Getting Started

1
Finding the codes (Github)
2
A Look at the Projects

Intro to Tensors - PyTorch

1
Intro
2
1 Dimensional Tensors
3
Vector Operations
4
2 Dimensional Tensors
5
Slicing 3D Tensors
6
Matrix Multiplication
7
Gradient with PyTorch
8
Outro

Linear Regression - PyTorch

1
Intro
2
Making Predictions
3
Linear Class
4
Custom Modules
5
Creating Dataset
6
Loss Function
7
Gradient Descent
8
Mean Squared Error
9
Training - Code Implementation
10
Getting Weird Results?
11
Outro

Perceptrons - PyTorch

1
Intro
2
What is Deep Learning
3
Creating Dataset
4
Perceptron Model
5
Model Setup
6
Model Training
7
Model Testing
8
Outro

Deep Neural Networks - PyTorch

1
Intro
2
Non-Linear Boundaries
3
Architecture
4
Feedforward Process
5
Error Function
6
Backpropagation
7
Code Implementation
8
Testing Model
9
Outro

Image Recognition - PyTorch

1
Intro
2
MNIST Dataset
3
Training and Test Datasets
4
Image Transforms
5
Important Update - Bug fix
6
Neural Network Implementation
7
Neural Network Validation
8
Test Links
9
Final Tests
10
A note on adjusting batch size
11
Outro

Convolutional Neural Networks - PyTorch

1
Convolutions and MNIST
2
Convolutional Layer
3
Convolutions II
4
Pooling
5
Fully Connected Network
6
Neural Network Implementation with PyTorch
7
Model Training with PyTorch

CIFAR 10 Classification - PyTorch

1
The CIFAR 10 Dataset
2
Testing LeNet
3
Hyperparameter Tuning
4
Data Augmentation

Transfer Learning - PyTorch

1
Pre-trained Sophisticated Models
2
Github Link for Dataset
3
AlexNet and VGG16

Style Transfer - PyTorch

1
Recommended Paper to Read (Optional)
2
VGG 19
3
Images Required for Next Lesson (Resource)
4
Image Transforms
5
Feature Extraction
6
2nd Optional Paper to Read
7
The Gram Matrix
8
Optimization
9
Content and Style Images
10
Style Transfer with Video
11
Goodbye, for now

All Source Codes

1
Intro
2
Linear Regression
3
Logistic Regression
4
Deep Neural Networks
5
MNIST Classification
6
Convolutional Neural Networks
7
CIFAR 10
8
Transfer Learning
9
Style Transfer

Appendix A - Python Crash Course (Optional)

1
Overview
2
Anaconda Installation (Mac)
3
Anaconda Installation Windows
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13 hours on-demand video
20 articles
Full lifetime access
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Certificate of Completion